bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluations of the Peroxidative Susceptibilities of Cod Liver Oils by a 1H NMR Analysis Strategy: Peroxidative Resistivity of a Natural Collagenous and Biogenic Amine-Rich Fermented Product
Authors: Percival Benita C. , Wann Angela , Zbasnik Richard , Schlegel Vicki , Edgar Mark , Zhang Jie , Ampem Gilbert , Wilson Philippe , Le Gresley Adam , Naughton Declan , Grootveld Martin
Published: March 2020

High-resolution 1H nuclear magnetic resonance (NMR) analysis was employed to molecularly screen the lipid, lipid oxidation product (LOP), and antioxidant compositions of four natural (unrefined) cod liver oil (CLO) products. Products 1–3 were non-fermented CLOs, whilst Product 4 was isolated from pre-fermented cod livers. Supporting analytical data that were acquired included biogenic amine, flavanone, tannin, phenolic antioxidant, α-tocopherol, and oxygen radical absorbance capacity (ORAC) determinations by recommended HPLC, LC/MS/MS, or spectrophotometric methods. SDS-PAGE, HPLC, and 1H NMR analyses investigated and determined collagenous antioxidants and their molecular mass ranges. 1H NMR analysis of aldehydic LOPs was employed to explore the susceptibilities/resistivities of each CLO product to peroxidation that is induced by thermal stressing episodes (TSEs) at 180°C, or following prolonged (42 day) storage episodes at 4 and 23 °C. Product 4 displayed extremely high ORAC values, which were much greater than those of Products 1–3, and that were predominantly ascribable to significant levels of peroxidation-blocking and/or aldehyde-consuming collagenous polypeptides/peptides and ammoniacal agents therein. Significantly lower levels of toxic aldehydes were generated in the pre-fermented Product 4 during exposure to TSEs, or the above long-term storage episodes. These results confirmed the enhanced peroxidative resistivity of a fermented, antioxidant-fortified natural CLO product over those of non-fermented unrefined products. Product 4: Green Pasture Blue Ice™ Fermented Cod Liver Oil.
Effect of Exercise Duration on Postprandial Glycaemic and Insulinaemic Responses in Adolescents
Authors: Dring Karah J. , Cooper Simon B. , Williams Ryan A. , Morris John G. , Sunderland Caroline , Nevill Mary E.
Published: March 2020

High-intensity intermittent exercise (HIIE) is a potential intervention to manage hyperglycaemia and insulin resistance in adolescents. The aim of this study was to determine the optimum duration of HIIE to reduce postprandial glycaemic and insulinaemic responses in adolescents and the longevity of the response. Thirty-nine participants (12.4 ± 0.4 year) completed a 30- and 60-min exercise trial (Loughborough Intermittent Shuttle Test) and a rested control trial in a randomised crossover design. Capillary blood samples were taken at baseline, immediately and 1-h post-exercise; and 30, 60 and 120 min following a standardised lunch (day one) and a standardised breakfast 24-h post-exercise. Plasma insulin total area under the curve (tAUC) following lunch was lower following 60-min HIIE (21,754 ± 16,861 pmol·L−1 × 120 min, p = 0.032) and tended to be lower following 30-min HIIE (24,273 ± 16,131 pmol·L−1 × 120 min, p = 0.080), when compared with the resting condition (26,931 ± 21,634 pmol·L−1 × 120 min). Blood glucose concentration was lower 1-h post-exercise following 30-min HIIE (3.6 ± 0.6 mmol·L−1) when compared to resting (4.1 ± 0.9 mmol·L−1, p = 0.001). Blood glucose and plasma insulin concentration did not differ across trials on day two. Shorter bouts of HIIE (30-min), as well as a 60-min bout, reduced the postprandial insulinaemic response to lunch, an ecologically valid marker of insulin sensitivity. As the beneficial effects of HIIE were limited to 3 h post-exercise, adolescents are recommended to engage daily HIIE to enhance metabolic health.
Reference Values for Skeletal Muscle Mass – Current Concepts and Methodological Considerations
Authors: Walowski Carina O. , Braun Wiebke , Maisch Michael J. , Jensen Björn , Peine Sven , Norman Kristina , Müller Manfred J. , Bosy-Westphal Anja
Published: March 2020

Assessment of a low skeletal muscle mass (SM) is important for diagnosis of ageing and disease-associated sarcopenia and is hindered by heterogeneous methods and terminologies that lead to differences in diagnostic criteria among studies and even among consensus definitions. The aim of this review was to analyze and summarize previously published cut-offs for SM applied in clinical and research settings and to facilitate comparison of results between studies. Multiple published reference values for discrepant parameters of SM were identified from 64 studies and the underlying methodological assumptions and limitations are compared including different concepts for normalization of SM for body size and fat mass (FM). Single computed tomography or magnetic resonance imaging images and appendicular lean soft tissue by dual X-ray absorptiometry (DXA) or bioelectrical impedance analysis (BIA) are taken as a valid substitute of total SM because they show a high correlation with results from whole body imaging in cross-sectional and longitudinal analyses. However, the random error of these methods limits the applicability of these substitutes in the assessment of individual cases and together with the systematic error limits the accurate detection of changes in SM. Adverse effects of obesity on muscle quality and function may lead to an underestimation of sarcopenia in obesity and may justify normalization of SM for FM. In conclusion, results for SM can only be compared with reference values using the same method, BIA- or DXA-device and an appropriate reference population. Limitations of proxies for total SM as well as normalization of SM for FM are important content-related issues that need to be considered in longitudinal studies, populations with obesity or older subjects.
A Naturalistic Perspective on Knowledge How: Grasping Truths in a Practical Way
Authors: V. Felix Cathrine , Stephens Andreas
Published: March 2020

For quite some time, cognitive science has offered philosophy an opportunity to address central problems with an arsenal of relevant theories and empirical data. However, even among those naturalistically inclined, it has been hard to find a universally accepted way to do so. In this article, we offer a case study of how cognitive-science input can elucidate an epistemological issue that has caused extensive debate. We explore Jason Stanley’s idea of the practical grasp of a propositional truth and present naturalistic arguments against his reductive approach to knowledge. We argue that a plausible interpretation of cognitive-science input concerning knowledge—even if one accepts that knowledge how is partly propositional—must involve an element of knowing how to act correctly upon the proposition; and this element of knowing how to act correctly cannot itself be propositional.
Quantum Communications in Future Networks and Services
Authors: Manzalini Antonio
Published: March 2020

Over the last few years, we have witnessed an impressive growth of data traffic and a progressive Digital Transformation of Industry and Society: the deployment of the ultra-broadband and low latency network infrastructures (e.g., 5G) are leading to a global digitalization of several domains. These techno-economic trends are expected to continue and even accelerate in the next decade, at end of which, 6G and smart networks and services will be exploited. Innovation will continue to drive the global economy into the next decade. This paper draws some technology trends and applications scenarios for this horizon, where Quantum Optical Communications are likely to disrupt Information and Communications Technology (ICT) and Telecommunications. Among the enabling technologies and solutions moving in this direction, this paper briefly addresses: quantum optical switching and computing, THz-to-optical conversions and advanced metamaterials for smart radio-optical programmable environments and Artificial Intelligence. The paper concludes with the description of a future application scenario, called Quantum Optical Twin, where the above Quantum Optical Communications technologies are exploited to provide services such as: ultra-massive scale communications for connected spaces and ambient intelligence, holographic telepresence, tactile Internet, new paradigms of brain computer interactions, innovative forms of communications.
‘Make Your House like a Temple’: Gender, Space and Domestic Devotion in Medieval Florence
Authors: Lawless Catherine
Published: March 2020

This article will discuss domestic devotions by framing them in terms of devotions carried out in the home, defined by its opposition to ecclesiastical, consecrated space. It will examine how women, considered the laity par excellence through their inability to ever attain sacerdotal authority, were advised spiritually by mendicant friars on how to lead a Christian life according to their status as wives, widows or virgins. It will look at the devotional literature that was widespread in mercantile homes and the devotional images designed to move the soul. This discussion will attempt to show the tensions between ecclesiastical and domestic spaces; between the clergy and the laity, and between the corporeal and spiritual worlds of late medieval devotion. It will argue that, despite clerical unease with the female and domestic space, the importance accorded to female piety by the mendicant orders at the close of the Middle Ages was such that women were entrusted with key educational roles in the family, even leading to the astonishing affirmation of them as ‘preachers’ within the borders of their households.
The Promise and Challenge of Multireligious Peacebuilding in the 21st Century: A Myanmar Case Study
Authors: King Anna S. , Owen Mark
Published: March 2020

This article explores whether a relational approach to peacebuilding, shared multireligious perspectives and widening networks can bring sources of strength which enable positive peacebuilding and create grassroots, cross-community peace. While religious peacebuilding organizations have become the object of a burgeoning literature, the role of multireligious organisations in peacebuilding has received far less attention. The purpose of this paper is to redress this lack. By examining the influence, challenges and benefits of multireligious approaches to transnational peacebuilding, we hope to develop a sharper and more critically nuanced understanding of the potential role of multireligious organisations in global peacebuilding, and consider what, if anything, distinguishes them from secular and other faith-based organisations. We do so by analysing the impact of a project carried out in Myanmar by Religions for Peace. The project provides three case studies which offer unique opportunities to consider the limits and potential of multireligious grassroots interventions in conflict contexts with very different histories and cultural configurations.
Seasonal Adaptation of the Thermal-Based Two-Source Energy Balance Model for Estimating Evapotranspiration in a Semiarid Tree-Grass Ecosystem
Authors: Burchard-Levine Vicente , Nieto Héctor , Riaño David , Migliavacca Mirco , El-Madany Tarek S. , Perez-Priego Oscar , Carrara Arnaud , Martín M. Pilar
Published: March 2020

The thermal-based two-source energy balance (TSEB) model has accurately simulated energy fluxes in a wide range of landscapes with both remote and proximal sensing data. However, tree-grass ecosystems (TGE) have notably complex heterogeneous vegetation mixtures and dynamic phenological characteristics presenting clear challenges to earth observation and modeling methods. Particularly, the TSEB modeling structure assumes a single vegetation source, making it difficult to represent the multiple vegetation layers present in TGEs (i.e., trees and grasses) which have different phenological and structural characteristics. This study evaluates the implementation of TSEB in a TGE located in central Spain and proposes a new strategy to consider the spatial and temporal complexities observed. This was based on sensitivity analyses (SA) conducted on both primary remote sensing inputs (local SA) and model parameters (global SA). The model was subsequently modified considering phenological dynamics in semi-arid TGEs and assuming a dominant vegetation structure and cover (i.e., either grassland or broadleaved trees) for different seasons (TSEB-2S). The adaptation was compared against the default model and evaluated against eddy covariance (EC) flux measurements and lysimeters over the experimental site. TSEB-2S vastly improved over the default TSEB performance decreasing the mean bias and root-mean-square-deviation (RMSD) of latent heat (LE) from 40 and 82 W m−2 to −4 and 59 W m−2, respectively during 2015. TSEB-2S was further validated for two other EC towers and for different years (2015, 2016 and 2017) obtaining similar error statistics with RMSD of LE ranging between 57 and 63 W m−2. The results presented here demonstrate a relatively simple strategy to improve water and energy flux monitoring over a complex and vulnerable landscape, which are often poorly represented through remote sensing models.
The VEI 2 Christmas 2018 Etna Eruption: A Small But Intense Eruptive Event or the Starting Phase of a Larger One?
Authors: Calvari Sonia , Bilotta Giuseppe , Bonaccorso Alessandro , Caltabiano Tommaso , Cappello Annalisa , Corradino Claudia , Del Negro Ciro , Ganci Gaetana , Neri Marco , Pecora Emilio , Salerno Giuseppe G. , Spampinato Letizia
Published: March 2020

The Etna flank eruption that started on 24 December 2018 lasted a few days and involved the opening of an eruptive fissure, accompanied by a seismic swarm and shallow earthquakes, significant SO2 flux release, and by large and widespread ground deformation, especially on the eastern flank of the volcano. Lava fountains and ash plumes from the uppermost eruptive fissure accompanied the opening stage, causing disruption to Catania International Airport, and were followed by a quiet lava effusion within the barren Valle del Bove depression until 27 December. This was the first flank eruption to occur at Etna in the last decade, during which eruptive activity was confined to the summit craters and resulted in lava fountains and lava flow output from the crater rims. In this paper, we used ground and satellite remote sensing techniques to describe the sequence of events, quantify the erupted volumes of lava, gas, and tephra, and assess volcanic hazards.
Recurrence Analysis of Vegetation Indices for Highlighting the Ecosystem Response to Drought Events: An Application to the Amazon Forest
Authors: Semeraro Teodoro , Luvisi Andrea , Lillo Antonio O. , Aretano Roberta , Buccolieri Riccardo , Marwan Norbert
Published: March 2020

Forests are important in sequestering CO2 and therefore play a significant role in climate change. However, the CO2 cycle is conditioned by drought events that alter the rate of photosynthesis, which is the principal physiological action of plants in transforming CO2 into biological energy. This study applied recurrence quantification analysis (RQA) to describe the evolution of photosynthesis-related indices to highlight disturbance alterations produced by the Atlantic Multidecadal Oscillation (AMO, years 2005 and 2010) and the El Niño-Southern Oscillation (ENSO, year 2015) in the Amazon forest. The analysis was carried out using Moderate Resolution Imaging Spectroradiometer (MODIS) images to build time series of the enhanced vegetation index (EVI), the normalized difference water index (NDWI), and the land surface temperature (LST) covering the period 2001–2018. The results did not show significant variations produced by AMO throughout the study area, while a disruption due to the global warming phase linked to the extreme ENSO event occurred, and the forest was able to recover. In addition, spatial differences in the response of the forest to the ENSO event were found. These findings show that the application of RQA to the time series of vegetation indices supports the evaluation of the forest ecosystem response to disruptive events. This approach provides information on the capacity of the forest to recover after a disruptive event and, therefore is useful to estimate the resilience of this particular ecosystem.
Evaluation of Deep Learning Techniques for Deforestation Detection in the Brazilian Amazon and Cerrado Biomes From Remote Sensing Imagery
Authors: Ortega Adarme Mabel , Queiroz Feitosa Raul , Nigri Happ Patrick , Aparecido De Almeida Claudio , Rodrigues Gomes Alessandra
Published: March 2020

Deforestation is one of the major threats to natural ecosystems. This process has a substantial contribution to climate change and biodiversity reduction. Therefore, the monitoring and early detection of deforestation is an essential process for preservation. Techniques based on satellite images are among the most attractive options for this application. However, many approaches involve some human intervention or are dependent on a manually selected threshold to identify regions that suffer deforestation. Motivated by this scenario, the present work evaluates Deep Learning-based strategies for automatic deforestation detection, namely, Early Fusion (EF), Siamese Network (SN), and Convolutional Support Vector Machine (CSVM) as well as Support Vector Machine (SVM), used as the baseline. The target areas are two regions with different deforestation patterns: the Amazon and Cerrado biomes in Brazil. The experiments used two co-registered Landsat 8 images acquired at different dates. The strategies based on Deep Learning achieved the best performance in our analysis in comparison with the baseline, with SN and EF superior to CSVM and SVM. In the same way, a reduction of the salt-and-pepper effect in the generated probabilistic change maps was noticed as the number of training samples increased. Finally, the work assesses how the methods can reduce the time invested in the visual inspection of deforested areas.
Evaluation of Global Decametric-Resolution LAI, FAPAR and FVC Estimates Derived from Sentinel-2 Imagery
Authors: Hu Qiong , Yang Jingya , Xu Baodong , Huang Jianxi , Memon Muhammad Sohail , Yin Gaofei , Zeng Yelu , Zhao Jing , Liu Ke
Published: March 2020

Global biophysical products at decametric resolution derived from Sentinel-2 imagery have emerged as a promising dataset for fine-scale ecosystem modeling and agricultural monitoring. Evaluating uncertainties of different Sentinel-2 biophysical products over various regions and vegetation types is pivotal in the application of land surface models. In this study, we quantified the performance of Sentinel-2-derived Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), and Fractional Vegetation Cover (FVC) estimates using global ground observations with consistent measurement criteria. Our results show that the accuracy of vegetation and non-vegetated classification based on Sentinel-2 surface reflectance products is greater than 95%, which indicates the vegetation identification is favorable for the practical application of biophysical estimates, as several LAI, FAPAR, and FVC retrievals were derived for non-vegetated pixels. The rate of best retrievals is similar between LAI and FAPAR estimates, both accounting for 87% of all vegetation pixels, while it is almost 100% for FVC estimates. Additionally, the Sentinel-2 FAPAR and FVC estimates agree well with ground-measurements-derived (GMD) reference maps, whereas a large discrepancy is observed for Sentinel-2 LAI estimates by comparing with both GMD effective LAI (LAIe) and actual LAI (LAI) reference maps. Furthermore, the uncertainties of Sentinel-2 LAI, FAPAR and FVC estimates are 1.09 m2/m2, 1.14 m2/m2, 0.13 and 0.17 through comparisons to ground LAIe, LAI, FAPAR, and FVC measurements, respectively. Given the temporal difference between Sentinel-2 observations and ground measurements, Sentinel-2 LAI estimates are more consistent with LAIe than LAI values. The robustness of evaluation results can be further improved as long as more multi-temporal ground measurements across different regions are obtained. Overall, this study provides fundamental information about the performance of Sentinel-2 LAI, FAPAR, and FVC estimates, which imbues our confidence in the broad applications of these decametric products.
Vertical Distribution of Arctic Methane in 2009–2018 Using Ground-Based Remote Sensing
Authors: Karppinen Tomi , Lamminpää Otto , Tukiainen Simo , Kivi Rigel , Heikkinen Pauli , Hatakka Juha , Laine Marko , Chen Huilin , Lindqvist Hannakaisa , Tamminen Johanna
Published: March 2020

We analyzed the vertical distribution of atmospheric methane (CH 4 ) retrieved from measurements by ground-based Fourier Transform Spectrometer (FTS) instrument in Sodankylä, Northern Finland. The retrieved dataset covers 2009–2018. We used a dimension reduction retrieval method to extract the profile information, since each measurement contains around three pieces of information about the profile shape between 0 and 40 km. We compared the retrieved profiles against Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) satellite measurements and AirCore balloon-borne profile measurements. Additional comparison at the lowest tropospheric layer was done against in-situ measurements from a 50-m-high mast. In general, the ground-based FTS and ACE-FTS profiles agreed within 10% below 20 km and within 30% in the stratosphere between 20 and 40 km. Our method was able to accurately capture reduced methane concentrations inside the polar vortex in the Arctic stratosphere. The method produced similar trend characteristics as the reference instruments even when a static prior profile was used. Finally, we analyzed the time series of the CH 4 profile datasets and estimated the trend using the dynamic linear model (DLM).
Impact of Calibrating Filtering Algorithms on the Quality of LiDAR-Derived DTM and on Forest Attribute Estimation through Area-Based Approach
Authors: Cosenza Diogo N. , Gomes Pereira Luísa , Guerra-Hernández Juan , Pascual Adrián , Soares Paula , Tomé Margarida
Published: March 2020

Ground point filtering of the airborne laser scanning (ALS) returns is crucial to derive digital terrain models (DTMs) and to perform ALS-based forest inventories. However, the filtering calibration requires considerable knowledge from users, who normally perform it by trial and error without knowing the impacts of the calibration on the produced DTM and the forest attribute estimation. Therefore, this work aims at calibrating four popular filtering algorithms and assessing their impact on the quality of the DTM and the estimation of forest attributes through the area-based approach. The analyzed filters were the progressive triangulated irregular network (PTIN), weighted linear least-squares interpolation (WLS) multiscale curvature classification (MCC), and the progressive morphological filter (PMF). The calibration was established by the vertical DTM accuracy, the root mean squared error (RMSE) using 3240 high-accuracy ground control points. The calibrated parameter sets were compared to the default ones regarding the quality of the estimation of the plot growing stock volume and the dominant height through multiple linear regression. The calibrated parameters allowed for producing DTM with RMSE varying from 0.25 to 0.26 m, against a variation from 0.26 to 0.30 m for the default parameters. The PTIN was the least affected by the calibration, while the WLS was the most affected. Compared to the default parameter sets, the calibrated sets resulted in dominant height equations with comparable accuracies for the PTIN, while WLS, MCC, and PFM reduced the models’ RMSE by 6.5% to 10.6%. The calibration of PTIN and MCC did not affect the volume estimation accuracy, whereas calibrated WLS and PMF reduced the RMSE by 3.4% to 7.9%. The filter calibration improved the DTM quality for all filters and, excepting PTIN, the filters increased the quality of forest attribute estimation, especially in the case of dominant height.
Analyzing the Angle Effect of Leaf Reflectance Measured by Indoor Hyperspectral Light Detection and Ranging (LiDAR)
Authors: Hu Peilun , Huang Huaguo , Chen Yuwei , Qi Jianbo , Li Wei , Jiang Changhui , Wu Haohao , Tian Wenxin , Hyyppä Juha
Published: March 2020

Hyperspectral light detection and ranging (LiDAR) (HSL) combines the characteristics of hyperspectral imaging and LiDAR techniques into a single instrument without any data registration. It provides more information than hyperspectral imaging or LiDAR alone in the extraction of vegetation physiological and biochemical parameters. However, the laser pulse intensity is affected by the incident angle, and its effect on HSL has not yet been fully explored. It is important for employing HSL to investigate vegetation properties. The aim of this paper is to study the incident angle effect of leaf reflectance with HSL and build a model about this impact. In this paper, we studied the angle effect of leaf reflectance from indoor HSL measurements of individual leaves from four typical tree species in Beijing. We observed that (a) the increasing of incident angle decreases the leaf reflectance; (b) the leaf spectrum observed by HSL from 650 to 1000 nm with 10 nm spectral resolution (36 channels) are consistent with those that measured by Analytica Spectra Devices (ASD) spectrometer (R2 = 0.9472 ~ 0.9897); (c) the specular reflection is significant in the red bands, and clear non-Lambertian characteristics are observed. In the near-infrared, there is little specular reflection, but it follows the Lambert-scattering law. We divided the whole band (650–1000 nm) into six bands and established an empirical model to correct the influence of angle effect on the reflectance of the leaf for HSL applications. In the future, the calibration of HSL measurements applied for other targets will be studied by rigorous experiments and modelling.
Complex Field Network Coding for Multi-Source Multi-Relay Single-Destination UAV Cooperative Surveillance Networks
Authors: Xue Rui , Han Lu , Chai Huisi
Published: March 2020

Relay-based cooperative communication for unmanned aerial vehicle (UAV) networks can obtain spatial diversity gains, expand coverage, and potentially increase the network capacity. A multi-source multi-relay single-destination structure is the main topology structure for UAV cooperative surveillance networks, which is similar to the structure of network coding (NC). Compared with conventional NC schemes, complex field network coding (CFNC) can achieve a higher throughput and is introduced to surveillance networks in this paper. According to whether there is a direct communication link between any source drone and the destination, the information transfer mechanism at the downlink is set to one of two modes, either mixed or relay transmission, and two corresponding irregular topology structures for CFNC-based networks are proposed. Theoretical analysis and simulation results with an additive white Gaussian noise (AWGN) channel show that the CFNC obtains a throughput as high as 1/2 symbol per source per channel use. Moreover, the CFNC applied to the proposed irregular structures under the two transmission modes can achieve better reliability due to full diversity gain as compared to that based on the regular structure. Moreover, the reliability of the CFNC scheme can continue to be improved by combining channel coding and modulation techniques at the expense of rate loss.
Linear and Circular UWB Millimeter-Wave and Terahertz Monostatic Near-Field Synthetic Aperture Imaging
Authors: Solano-Perez Jose Antonio , Martínez-Inglés María-Teresa , Molina-Garcia-Pardo Jose-Maria , Romeu Jordi , Jofre Lluis , Rodríguez José-Víctor , Mateo-Aroca Antonio
Published: March 2020

Millimeter-wave and terahertz frequencies offer unique characteristics to simultaneously obtain good spatial resolution and penetrability. In this paper, a robust near-field monostatic focusing technique is presented and successfully applied for the internal imaging of different penetrable geometries. These geometries and environments are related to the growing need to furnish new vehicles with radar-sensing devices that can visualize their surroundings in a clear and robust way. Sub-millimeter-wave radar sensing offers enhanced capabilities in providing information with a high level of accuracy and quality, even under adverse weather conditions. The aim of this paper was to research the capability of this radar system for imaging purposes from an analytical and experimental point of view. Two sets of measurements, using reference targets, were performed in the W band at 100 GHz (75 to 110 GHz) and terahertz band at 300 GHz (220 to 330 GHz). The results show spatial resolutions of millimeters in both the range (longitudinal) and the cross-range (transversal) dimensions for the two different imaging geometries in terms of the location of the transmitter and receiver (frontal or lateral views). The imaging quality in terms of spatial accuracy and target material parameter was investigated and optimized.
Simplified Fréchet Distance for Generative Adversarial Nets
Authors: Kim Chung-Il , Kim Meejoung , Jung Seungwon , Hwang Eenjun
Published: March 2020

We introduce a distance metric between two distributions and propose a Generative Adversarial Network (GAN) model: the Simplified Fréchet distance (SFD) and the Simplified Fréchet GAN (SFGAN). Although the data generated through GANs are similar to real data, GAN often undergoes unstable training due to its adversarial structure. A possible solution to this problem is considering Fréchet distance (FD). However, FD is unfeasible to realize due to its covariance term. SFD overcomes the complexity so that it enables us to realize in networks. The structure of SFGAN is based on the Boundary Equilibrium GAN (BEGAN) while using SFD in loss functions. Experiments are conducted with several datasets, including CelebA and CIFAR-10. The losses and generated samples of SFGAN and BEGAN are compared with several distance metrics. The evidence of mode collapse and/or mode drop does not occur until 3000k steps for SFGAN, while it occurs between 457k and 968k steps for BEGAN. Experimental results show that SFD makes GANs more stable than other distance metrics used in GANs, and SFD compensates for the weakness of models based on BEGAN-based network structure. Based on the experimental results, we can conclude that SFD is more suitable for GAN than other metrics.
High-Level Path Planning for an Autonomous Sailboat Robot Using Q-Learning
Authors: Silva Junior Andouglas Gonçalves da Silva , Santos Davi Henrique dos , Negreiros Alvaro Pinto Fernandes de , Silva João Moreno Vilas Boas de Souza , Gonçalves Luiz Marcos Garcia
Published: March 2020

Path planning for sailboat robots is a challenging task particularly due to the kinematics and dynamics modelling of such kinds of wind propelled boats. The problem is divided into two layers. The first one is global were a general trajectory composed of waypoints is planned, which can be done automatically based on some variables such as weather conditions or defined by hand using some human–robot interface (a ground-station). In the second local layer, at execution time, the global route should be followed by making the sailboat proceed between each pair of consecutive waypoints. Our proposal in this paper is an algorithm for the global, path generation layer, which has been developed for the N-Boat (The Sailboat Robot project), in order to compute feasible sailing routes between a start and a target point while avoiding dangerous situations such as obstacles and borders. A reinforcement learning approach (Q-Learning) is used based on a reward matrix and a set of actions that changes according to wind directions to account for the dead zone, which is the region against the wind where the sailboat can not gain velocity. Our algorithm generates straight and zigzag paths accounting for wind direction. The path generated also guarantees the sailboat safety and robustness, enabling it to sail for long periods of time, depending only on the start and target points defined for this global planning. The result is the development of a complete path planner algorithm that, together with the local planner solved in previous work, can be used to allow the final developments of an N-Boat making it a fully autonomous sailboat.
A Repeated Game Freeway Lane Changing Model
Authors: Kang Kyungwon , Rakha Hesham A.
Published: March 2020

Lane changes are complex safety- and throughput-critical driver actions. Most lane-changing models deal with lane-changing maneuvers solely from the merging driver’s standpoint and thus ignore driver interaction. To overcome this shortcoming, we develop a game-theoretical decision-making model and validate the model using empirical merging maneuver data at a freeway on-ramp. Specifically, this paper advances our repeated game model by using updated payoff functions. Validation results using the Next Generation SIMulation (NGSIM) empirical data show that the developed game-theoretical model provides better prediction accuracy compared to previous work, giving correct predictions approximately 86% of the time. In addition, a sensitivity analysis demonstrates the rationality of the model and its sensitivity to variations in various factors. To provide evidence of the benefits of the repeated game approach, which takes into account previous decision-making results, a case study is conducted using an agent-based simulation model. The proposed repeated game model produces superior performance to a one-shot game model when simulating actual freeway merging behaviors. Finally, this lane change model, which captures the collective decision-making between human drivers, can be used to develop automated vehicle driving strategies.
Fabrication of Microfluidic Chips Based on an EHD-Assisted Direct Printing Method
Authors: Chi Xiang , Zhang Xinyu , Li Zongan , Yuan Zhe , Zhu Liya , Zhang Feng , Yang Jiquan
Published: March 2020

Microfluidic chips have been widely used in many areas such as biology, environmental monitoring, and micromixing. With the increasing popularity and complexity of microfluidic systems, rapid and convenient approaches for fabricating microfluidic chips are necessary. In this study, a method based on EHD (electrohydrodynamic)-assisted direct printing is proposed. Firstly, the principle of EHD-assisted direct printing was analyzed. The influence of the operating voltage and moving speed of the work table on the width of a paraffin wax model was studied. Then, two kinds of paraffin wax molds for micromixing with channel widths of 120 μm were prepared. A polydimethylsiloxane (PDMS) micromixer was fabricated by replicating the paraffin wax mold, and the micromixing of blue and yellow dye was realized. The results show that EHD-assisted direct printing can be used to make complex microscale structures, which has the potential to greatly simplify the manufacturing process.
Multiway PCA for Early Leak Detection in a Pipeline System of a Steam Boiler—Selected Case Studies
Authors: Swiercz Miroslaw , Mroczkowska Halina
Published: March 2020

In the paper the usability of the Multiway PCA (MPCA) method for early detection of leakages in the pipeline system of a steam boiler in a thermal-electrical power plant is presented. A long segment of measurements of selected process variables was divided into a series of “batches” (representing daily recordings of normal behavior of the plant) and used to create the MPCA model of a “healthy” system in a reduced space of three principal components (PC). The periodically updated MPCA model was used to establish the confidence ellipsoid for the “healthy” system in the PC coordinates. The staff’s decision of the probable leak detection is supported by comparison of the current location of the operating point (on the “fault trajectory”) with the boundaries of the confidence ellipsoid. It must be emphasized that due to daily and seasonal changes of heat/electricity demands, the process variables have substantially greater variability than in the examples of batch processes studied in literature. Despite those real challenges for the MPCA method, numerical examples confirmed that the presented approach was able to foresee the leaks earlier than the operator, typically 3–5 days before the boiler shutdown. The presented methodology may be useful in implementation of an on-line system, developed to improve safety and maintenance of boilers in a thermal-electrical power plant.
Low-Complexity Soft-Output Signal Detection Based on Improved Kaczmarz Iteration Algorithm for Uplink Massive MIMO System
Authors: Wu Hebiao , Shen Bin , Zhao Shufeng , Gong Peng
Published: March 2020

For multi-user uplink massive multiple input multiple output (MIMO) systems, minimum mean square error (MMSE) criterion-based linear signal detection algorithm achieves nearly optimal performance, on condition that the number of antennas at the base station is asymptotically large. However, it involves prohibitively high complexity in matrix inversion when the number of users is getting large. A low-complexity soft-output signal detection algorithm based on improved Kaczmarz method is proposed in this paper, which circumvents the matrix inversion operation and thus reduces the complexity by an order of magnitude. Meanwhile, an optimal relaxation parameter is introduced to further accelerate the convergence speed of the proposed algorithm and two approximate methods of calculating the log-likelihood ratios (LLRs) for channel decoding are obtained as well. Analysis and simulations verify that the proposed algorithm outperforms various typical low-complexity signal detection algorithms. The proposed algorithm converges rapidly and achieves its performance quite close to that of the MMSE algorithm with only a small number of iterations.
Segment-Resolved Gas Concentration Measurements by a Time Domain Multiplexed Dual Comb Method
Authors: Chen Xinyi , Zhang Weipeng , Zhang Yujia , Lu Minjian , Li Yan , Wei Haoyun
Published: March 2020

Locating gas concentration changes in widespread locations can be conducive to environmental atmospheric detection, gas emissions monitoring, production process control, etc. A time domain multiplexed dual-comb system for segment-resolved gas concentration measurement is reported in this work. Both absorption spectra and path lengths for multiple path-segments in a target path can be derived from the time domain separated interferograms and then the equivalent gas concentrations in each segment can be retrieved separately. A benchtop experiment aiming at a target path with three path-segments of different gases has been demonstrated. The relative deviation of gas concentration retrieval is 1.08% in 1 s. Besides, additional numerical simulations prove that the crosstalk between the interference signals affects the spectrum analysis by no more than 0.1% for a kilometer-long atmospheric absorption detection. Therefore, achieving a gridded measurement of regional gas concentration in the open air can be foreseen using this method.
Implementation of a Mobile Platform Based on Fiber Bragg Grating Sensors for Automotive Traffic Monitoring
Authors: Yuksel Kivilcim , Kinet Damien , Chah Karima , Caucheteur Christophe
Published: March 2020

Instrumentation techniques, implementation and installation methods are major concerns in today’s distributed and quasi-distributed monitoring applications using fiber optic sensors. Although many successful traffic monitoring experiments have been reported using Fiber Bragg Gratings (FBGs), there has been no standardized solution proposed so far to have FBG seamlessly implemented in roads. In this work, we investigate a mobile platform including FBG sensors that can be positioned on roads for the purpose of vehicle speed measurements. The experimental results prove the efficiency of the proposed platform, providing a perspective toward weigh-in-motion systems.
Application of Wearables to Facilitate Virtually Supervised Intradialytic Exercise for Reducing Depression Symptoms
Authors: Zhou He , Al-Ali Fadwa , Kang Gu Eon , Hamad Abdullah I. , Ibrahim Rania A. , Talal Talal K. , Najafi Bijan
Published: March 2020

Regular exercise can reduce depression. However, the uptake of exercise is limited in patients with end-stage renal disease undergoing hemodialysis. To address the gap, we designed a gamified non-weight-bearing intradialytic exercise program (exergame). The intradialytic exergame is virtually supervised based on its interactive feedback via wearable sensors attached on lower extremities. We examined the effectiveness of this program to reduce depression symptoms compared to nurse-supervised intradialytic exercise in 73 hemodialysis patients (age = 64.5 ± 8.7years, BMI = 31.6 ± 7.6kg/m2). Participants were randomized into an exergame group (EG) or a supervised exercise group (SG). Both groups received similar exercise tasks for 4 weeks, with three 30 min sessions per week, during hemodialysis treatment. Depression symptoms were assessed at baseline and the fourth week using the Center for Epidemiologic Studies Depression Scale. Both groups showed a significant reduction in depression score (37%, p < 0.001, Cohen’s effect size d = 0.69 in EG vs. 41%, p < 0.001, d = 0.65 in SG) with no between-group difference for the observed effect (p > 0.050). The EG expressed a positive intradialytic exercise experience including fun, safety, and helpfulness of sensor feedback. Together, results suggested that the virtually supervised low-intensity intradialytic exergame is feasible during routine hemodialysis treatment. It also appears to be as effective as nurse-supervised intradialytic exercise to reduce depression symptoms, while reducing the burden of administrating exercise on dialysis clinics.
UWB/BLE Tracking System for Elderly People Monitoring
Authors: Kolakowski Jerzy , Djaja-Josko Vitomir , Kolakowski Marcin , Broczek Katarzyna
Published: March 2020

Localization systems are the source of data that allows to evaluate elderly person’s behaviour, to draw conclusions concerning his or her health status and wellbeing, and to detect emergency situations. The article contains a description of a system intended for elderly people tracking. Two novel solutions have been implemented in the system: a hybrid localization algorithm and a method for wireless anchor nodes synchronization. The algorithm fuses results of time difference of arrival and received signal strength measurements in ultrawideband (UWB) and Bluetooth Low Energy (BLE) radio interfaces, respectively. The system allows to change the intensity of UWB packets transmission to adapt localization accuracy and energy usage to current needs and applications. In order to simplify the system installation, communication between elements of the system infrastructure instead of wire interfaces is performed over wireless ones. The new wireless synchronization method proposed in the article consists in retransmission of UWB synchronization packets by selected anchor nodes. It allows for extension of the system coverage, which is limited by the short range of UWB transmission. The proposed solution was experimentally verified. The synchronization method was tested in a laboratory, and the whole system’s performance was investigated in a typical flat. Exemplary results of the tests performed with older adult participation in their own homes are also included.
Improving the Heading Accuracy in Indoor Pedestrian Navigation Based on a Decision Tree and Kalman Filter
Authors: Hu Guanghui , Zhang Weizhi , Wan Hong , Li Xinxin
Published: March 2020

In pedestrian inertial navigation, multi-sensor fusion is often used to obtain accurate heading estimates. As a widely distributed signal source, the geomagnetic field is convenient to provide sufficiently accurate heading angles. Unfortunately, there is a broad presence of artificial magnetic perturbations in indoor environments, leading to difficulties in geomagnetic correction. In this paper, by analyzing the spatial distribution model of the magnetic interference field on the geomagnetic field, two quantitative features have been found to be crucial in distinguishing normal magnetic data from anomalies. By leveraging these two features and the classification and regression tree (CART) algorithm, we trained a decision tree that is capable of extracting magnetic data from distorted measurements. Furthermore, this well-trained decision tree can be used as a reject gate in a Kalman filter. By combining the decision tree and Kalman filter, a high-precision indoor pedestrian navigation system based on a magnetically assisted inertial system is proposed. This system is then validated in a real indoor environment, and the results show that our system delivers state-of-the-art positioning performance. Compared to other baseline algorithms, an improvement of over 70% in the positioning accuracy is achieved.
Extended Kalman Filter with Reduced Computational Demands for Systems with Non-Linear Measurement Models
Authors: Kaniewski Piotr
Published: March 2020

The paper presents a method of computational complexity reduction in Extended Kalman Filters dedicated for systems with non-linear measurement models. Extended Kalman filters are commonly used in radio-location and radio-navigation for estimating an object’s position and other parameters of motion, based on measurements, which are non-linearly related to the object’s position. This non-linearity forces designers to use non-linear filters, such as the Extended Kalman Filter mentioned, where linearization of the system’s model is performed in every run of the filter’s loop. The linearization, consisting of calculating Jacobian matrices for non-linear functions in the dynamics and/or observation models, significantly increases the number of operations in comparison to the linear Kalman filter. The method proposed in this paper consists of analyzing a variability of Jacobians and performing the model linearization only when expected changes of those Jacobians exceed a preset threshold. With a properly chosen threshold value, the proposed filter modification leads to a significant reduction of its computational burden and does not noticeably increase its estimation errors. The paper describes a practical simulation-based method of determining the threshold. The accuracy of the filter for various threshold values was tested for simplified models of radar systems.
A Rapid and Nondestructive Approach for the Classification of Different-Age Citri Reticulatae Pericarpium Using Portable Near Infrared Spectroscopy
Authors: Li Pao , Zhang Xinxin , Li Shangke , Du Guorong , Jiang Liwen , Liu Xia , Ding Shenghua , Shan Yang
Published: March 2020

Citri Reticulatae Pericarpium (CRP), has been used in China for hundreds of years as a functional food and medicine. However, some short-age CRPs are disguised as long-age CRPs by unscrupulous businessmen in order to obtain higher profits. In this paper, a rapid and nondestructive method for the classification of different-age CRPs was established using portable near infrared spectroscopy (NIRS) in diffuse reflectance mode combination with appropriate chemometric methods. The spectra of outer skin and inner capsule of CRPs at different storage ages were obtained directly without destroying the samples. Principal component analysis (PCA) with single and combined spectral pretreatment methods was used for the classification of different-age CRPs. Furthermore, the data were pretreated with the PCA method, and Fisher linear discriminant analysis (FLD) with optimized pretreatment methods was discussed for improving the accuracy of classification. Data pretreatment methods can be used to eliminate the noise and background interference. The classification accuracy of inner capsule is better than that of outer skin data. Furthermore, the best results with 100% prediction accuracy can be obtained with FLD method, even without pretreatment.