bluuebunny/crossref_metadata_2025 huggingface dataset:
Barriers and Facilitators of Fruit and Vegetable Consumption in Renal Transplant Recipients, Family Members and Healthcare Professionals—A Focus Group Study
Authors: Boslooper-Meulenbelt Karin , Patijn Olga , Battjes-Fries Marieke C. E. , Haisma Hinke , Pot Gerda K. , Navis Gerjan J.
Published: October 2019

Low fruit and vegetable consumption is associated with poor outcomes after renal transplantation. Insufficient fruit and vegetable consumption is reported in the majority of renal transplant recipients (RTR). The aim of this study was to identify barriers and facilitators of fruit and vegetable consumption after renal transplantation and explore if certain barriers and facilitators were transplant-related. After purposive sampling, RTR (n = 19), their family members (n = 15) and healthcare professionals (n = 5) from a Dutch transplant center participated in seven focus group discussions (three each for RTR and family members, one with healthcare professionals). Transcripts were analyzed using social cognitive theory as conceptual framework and content analysis was used for identification of themes. Transplant-related barriers and facilitators were described separately. In categorizing barriers and facilitators, four transplant-related themes were identified: transition in diet (accompanied by, e.g., fear or difficulties with new routine), physical health (e.g., recovery of uremic symptoms), medication (e.g., cravings by prednisolone) and competing priorities after transplantation (e.g., social participation activities). Among the generic personal and environmental barriers and facilitators, food literacy and social support were most relevant. In conclusion, transplant-related and generic barriers and facilitators were identified for fruit and vegetable consumption in RTR. The barriers that accompany the dietary transition after renal transplantation may contribute to the generally poorer fruit and vegetable consumption of RTR. These findings can be used for the development of additional nutritional counseling strategies in renal transplant care.
Whole Transcriptome Analyses Reveal Differential mRNA and microRNA Expression Profiles in Primary Human Dermal Fibroblasts Infected with Clinical or Vaccine Strains of Varicella Zoster Virus
Authors: Oh Soo-Jin , Lim Sooyeon , Song Moon Jung , Ahn Jin Hyun , Lee Chan Hee , Shin Ok Sarah
Published: October 2019

Licensed live attenuated vaccines have been developed to prevent varicella zoster virus (VZV) infection, which causes chickenpox and shingles. The genomic sequences of both clinical- and vaccine-derived VZV strains have been analyzed previously. To further characterize the molecular signatures and complexity of wildtype (clinical) versus attenuated (vaccine-derived) VZV-mediated host cellular responses, we performed high-throughput next generation sequencing to quantify and compare the expression patterns of mRNAs and microRNAs (miRNAs) in primary human dermal fibroblasts (HDFs) infected with wildtype (YC01 low passage) and attenuated (YC01 high passage, SuduVax, and VarilRix) VZV strains. 3D-multidimensional scaling of the differentially expressed genes demonstrated the distinct grouping of wildtype and attenuated strains. In particular, we observed that HDFs infected with attenuated strains had more differentially expressed genes (DEGs) involved in the retinoic-acid inducible gene–I-like receptor and interferon-mediated signaling pathways compared with wildtype strains. Additionally, miRNA expression patterns were profiled following the infection of HDFs with VZV. Small RNA sequencing identified that several miRNAs were upregulated, including miR-146a-5p, which has been associated with other herpesvirus infections, whereas let-7a-3p was downregulated in both wildtype and attenuated VZV-infected cells. This study identified genes and miRNAs that may be essential in VZV pathogenesis.
Enzyme-Loaded Gel Core Nanostructured Lipid Carriers to Improve Treatment of Lysosomal Storage Diseases: Formulation and In Vitro Cellular Studies of Elosulfase Alfa-Loaded Systems
Authors: Álvarez J. , Herrero Filgueira Carolina , González Alexandre , Colón Mejeras Cristóbal , Beiras Iglesias Andrés , Tomatsu Shunji , Blanco Méndez José , Luzardo Álvarez Asteria , Couce María , Otero Espinar Francisco
Published: October 2019

Mucopolysaccharidosis IVA (Morquio A) is a rare inherited metabolic disease caused by deficiency of the lysosomal enzyme N-acetylgalatosamine-6-sulfate-sulfatase (GALNS). Until now, treatments employed included hematopoietic stem cell transplantation and enzyme replacement therapy (ERT); the latter being the most commonly used to treat mucopolysaccharidoses, but with serious disadvantages due to rapid degradation and clearance. The purpose of this study was to develop and evaluate the potential of nanostructured lipid carriers (NLCs) by encapsulating elosulfase alfa and preserving its enzyme activity, leading to enhancement of its biological effect in chondrocyte cells. A pegylated elosulfase alfa-loaded NLC was characterized in terms of size, ζ potential, structural lipid composition (DSC and XRD), morphology (TEM microscopy), and stability in human plasma. The final formulation was freeze-dried by selecting the appropriate cryoprotective agent. Viability assays confirmed that NLCs were non-cytotoxic to human fibroblasts. Imaging techniques (confocal and TEM) were used to assess the cellular uptake of NLCs loaded with elosulfase alfa. This study provides evidence that the encapsulated drug exhibits enzyme activity inside the cells. Overall, this study provides a new approach regarding NLCs as a promising delivery system for the encapsulation of elosulfase alfa or other enzymes and the preservation of its activity and stability to be used in enzymatic replacement therapy (ERT).
Molecular Analyses of the Distribution and Function of Diazotrophic Rhizobia and Methanotrophs in the Tissues and Rhizosphere of Non-Leguminous Plants
Authors: Yoneyama Tadakatsu , Terakado-Tonooka Junko , Bao Zhihua , Minamisawa Kiwamu
Published: October 2019

Biological nitrogen fixation (BNF) by plants and its bacterial associations represent an important natural system for capturing atmospheric dinitrogen (N2) and processing it into a reactive form of nitrogen through enzymatic reduction. The study of BNF in non-leguminous plants has been difficult compared to nodule-localized BNF in leguminous plants because of the diverse sites of N2 fixation in non-leguminous plants. Identification of the involved N2-fixing bacteria has also been difficult because the major nitrogen fixers were often lost during isolation attempts. The past 20 years of molecular analyses has led to the identification of N2 fixation sites and active nitrogen fixers in tissues and the rhizosphere of non-leguminous plants. Here, we examined BNF hotspots in six reported non-leguminous plants. Novel rhizobia and methanotrophs were found to be abundantly present in the free-living state at sites where carbon and energy sources were predominantly available. In the carbon-rich apoplasts of plant tissues, rhizobia such as Bradyrhizobium spp. microaerobically fix N2. In paddy rice fields, methane molecules generated under anoxia are oxidized by xylem aerenchyma-transported oxygen with the simultaneous fixation of N2 by methane-oxidizing methanotrophs. We discuss the effective functions of the rhizobia and methanotrophs in non-legumes for the acquisition of fixed nitrogen in addition to research perspectives.
Opposite Self-Folding Behavior of Polymeric Photoresponsive Actuators Enabled by a Molecular Approach
Authors: Martella Daniele , Nocentini Sara , Antonioli Diego , Laus Michele , Wiersma Diederik S. , Parmeggiani Camilla
Published: October 2019

The ability to obtain 3D polymeric objects by a 2D-to-3D shape-shifting method is very appealing for polymer integration with different materials, from metals in electronic devices to cells in biological studies. Such functional reshaping can be achieved through self-folding driven by a strain pattern designed into the molecular network. Among polymeric materials, liquid crystalline networks (LCNs) present an anisotropic molecular structure that can be exploited to tailor internal strain, resulting in a natural non-planar geometry when prepared in the form of flat films. In this article, we analyze the influence of different molecular parameters of the monomers on the spontaneous shape of the polymeric films and their deformation under different stimuli, such as heating or light irradiation. Modifying the alkilic chains of the crosslinkers is a simple and highly effective way to increase the temperature sensitivity of the final actuator, while modifying ester orientation on the aromatic core interestingly acts on the bending direction. Combining such effects, we have demonstrated that LCN stripes made of different monomeric mixtures originate complex non-symmetric deformation under light activation, thus opening up new applications in photonic and robotics.
Eco-Friendly β-cyclodextrin and Linecaps Polymers for the Removal of Heavy Metals
Authors: Rubin Pedrazzo Alberto , Smarra Alessandra , Caldera Fabrizio , Musso Giorgia , Dhakar Nilesh Kumar , Cecone Claudio , Hamedi Asma , Corsi Ilaria , Trotta Francesco
Published: October 2019

Environment-friendly nanosponges, having a high content of carboxyl groups, were synthesized by crosslinking β-cyclodextrin and linecaps, a highly soluble pea starch derivative, with citric acid in water. Additionally, pyromellitic nanosponges were prepared by reacting β-cyclodextrin and linecaps with pyromellitic dianhydride in dimethyl sulfoxide and used in comparison with the citric nanosponges. After ion-exchange of the carboxyl groups H+ with sodium ions, the ability of the nanosponges to sequester heavy metal cations was investigated. At a metal concentration of 500 ppm, the pyromellitate nanosponges exhibited a higher retention capacity than the citrate nanosponges. At lower metal concentrations (≤50 ppm) both the citrate and the pyromellitate nanosponges showed high retention capacities (up to 94% of the total amount of metal), while, in the presence of interfering sea water salts, the citrate nanosponges were able to selectively adsorb a significantly higher amount of heavy metals than the pyromellitate nanosponges, almost double in the case of Cu2+.
Effective Heat Transfer Pathways of Thermally Conductive Networks Formed by One-Dimensional Carbon Materials with Different Sizes
Authors: Lee Yun Seon , Lee Seung-Yong , Kim Keun Soo , Noda Suguru , Shim Sang Eun , Yang Cheol-Min
Published: October 2019

We investigated the heat transfer behavior of thermally conductive networks with one-dimensional carbon materials to design effective heat transfer pathways for hybrid filler systems of polymer matrix composites. Nano-sized few-walled carbon nanotubes (FWCNTs) and micro-sized mesophase pitch-based carbon fibers (MPCFs) were used as the thermally conductive materials. The bulk density and thermal conductivity of the FWCNT films increased proportionally with the ultrasonication time due to the enhanced dispersibility of the FWCNTs in an ethanol solvent. The ultrasonication-induced densification of the FWCNT films led to the effective formation of filler-to-filler connections, resulting in improved thermal conductivity. The thermal conductivity of the FWCNT-MPCF hybrid films was proportional to the MPCF content (maximum thermal conductivity at an MPCF content of 60 wt %), indicating the synergistic effect on the thermal conductivity enhancement. Moreover, the MPCF-to-MPCF heat transfer pathways in the FWCNT-MPCF hybrid films were the most effective in achieving high thermal conductivity due to the smaller interfacial area and shorter heat transfer pathway of the MPCFs. The FWCNTs could act as thermal bridges between neighboring MPCFs for effective heat transfer. Furthermore, the incorporation of Ag nanoparticles of approximately 300 nm into the FWCNT-MPCF hybrid film dramatically enhanced the thermal conductivity, which was closely related to a decreased thermal interfacial resistance at the intersection points between the materials. Epoxy-based composites loaded with the FWCNTs, MPCFs, FWCNT-MPCF hybrids, and FWCNT-MPCF-Ag hybrid fillers were also fabricated. A similar trend in thermal conductivity was observed in the polymer matrix composite with carbon-based hybrid films.
Bifurcation Characteristic Research on the Load Vertical Vibration of a Hydraulic Automatic Gauge Control System
Authors: Zhu Yong , Tang Shengnan , Wang Chuan , Jiang Wanlu , Yuan Xiaoming , Lei Yafei
Published: October 2019

As the core control system of a rolling mill, the hydraulic automatic gauge control (HAGC) system is key to ensuring a rolling process with high speed, high precision and high reliability. However, a HAGC system is typically a mechanical-electric-hydraulic coupling system with nonlinear characteristics. The vertical vibration of the load easily occurs during the working process, which seriously affects the stability of the system and the causes are difficult to determine. In this work, the theory and method of nonlinear dynamics were employed. The load vertical vibration model of the HAGC system was established. Then, the multi-scale method was utilized to solve the obtained model, and the singularity theory was further applied to derive the transition set. Moreover, the research object of this article focused on some nonlinear factors such as excitation force, elastic force and damping force. The effects of the above feature parameters on bifurcation behavior were emphatically explored. The bifurcation characteristic of the load vertical vibration of the HAGC system was revealed. The research results indicate that the bifurcation curves in each sub-region, divided by the transition set, possess their own topological structure. The changes of the feature parameters, such as the nonlinear stiffness coefficient, liquid column height, nonlinear damping coefficient, and external excitation have an influence on the vibration amplitude of the HAGC system. By reasonably adjusting the nonlinear stiffness coefficient to effectively avoid the resonance region, the stability of the system will be facilitated. Furthermore, this is conducive to the system’s stability as it properly controls the size of the liquid column height of the hydraulic cylinder. The appropriate nonlinear damping coefficient can decrease the unstable area, which is beneficial to the stability of the system. However, large external excitation is not conducive to the stability of the system.
Stagnation Point Flow with Time-Dependent Bionanofluid Past a Sheet: Richardson Extrapolation Technique
Authors: Naganthran Kohilavani , Basir Md Faisal Md , Alharbi Sayer Obaid , Nazar Roslinda , Alwatban Anas M. , Tlili Iskander
Published: October 2019

The study of laminar flow of heat and mass transfer over a moving surface in bionanofluid is of considerable interest because of its importance for industrial and technological processes such as fabrication of bio-nano materials and thermally enhanced media for bio-inspired fuel cells. Hence, the present work deals with the unsteady bionanofluid flow, heat and mass transfer past an impermeable stretching/shrinking sheet. The appropriate similarity solutions transform the boundary layer equations with three independent variables to a system of ordinary differential equations with one independent variable. The finite difference coupled with the Richardson extrapolation technique in the Maple software solves the reduced system, numerically. The rate of heat transfer is found to be higher when the flow is decelerated past a stretching sheet. It is understood that the state of shrinking sheet limits the rate of heat transfer and the density of the motile microorganisms in the stagnation region.
Stimuli-Sensitive Cell Penetrating Peptide-Modified Nanocarriers
Authors: Perche Federico
Published: October 2019

The integration of drugs into nanocarriers favorably altered their pharmacodynamics and pharmacokinetics compared to free drugs, and increased their therapeutic index. However, selective cellular internalization in diseased tissues rather than normal tissues still presents a formidable challenge. In this chapter I will cover solutions involving environment-responsive cell-penetrating peptides (CPPs). I will discuss properties of CPPs as universal cellular uptake enhancers, and the modifications imparted to CPP-modified nanocarriers to confine CPP activation to diseased tissues.
Performance Optimization of High Specific Speed Centrifugal Pump Based on Orthogonal Experiment Design Method
Authors: Li Zikang , Ding Hongchang , Shen Xiao , Jiang Yongming
Published: October 2019

A high specific speed centrifugal pump is used in the situation of large flow and low head. Centrifugal pump parameters need to be optimized in order to raise its head and efficiency under off-design conditions. In this study, the orthogonal experiment design method is adopted to optimize the performance of centrifugal pump basing on three parameters, namely, blade outlet width b2, blade outlet angle β2 and blade wrap angle φ. First, the three-dimensional model of the centrifugal pump is established by CFturbo and SolidWorks. Then nine different schemes are designed by using orthogonal table, and numerical simulation is carried out in CFX15.0. The final optimized combination of parameters is b2 = 24 mm, β2 = 24°, φ = 112°. Under the design condition, the head and efficiency of the optimized centrifugal pump are appropriately improved, the increments of which are 0.74 m and 0.48%, respectively. However, the efficiency considerably increases at high flow rates, with an increase of 6.9% at 1.5 Qd. The anti-cavitation performance of the optimized centrifugal pump is also better than the original pump. The results in this paper can provide references for parameter selection (b2, β2, φ) in the centrifugal pump design.
Classification of Black Plastics Waste Using Fluorescence Imaging and Machine Learning
Authors: Gruber Florian , Grählert Wulf , Wollmann Philipp , Kaskel Stefan
Published: October 2019

This work contributes to the recycling of technical black plastic particles, for example from the automotive or electronics industries. These plastics cannot yet be sorted with sufficient purity (up to 99.9%), which often makes economical recycling impossible. As a solution to this problem, imaging fluorescence spectroscopy with additional illumination in the near infrared spectral range in combination with classification by machine learning or deep learning classification algorithms is here investigated. The algorithms used are linear discriminant analysis (LDA), k-nearest neighbour classification (kNN), support vector machines (SVM), ensemble models with decision trees (ENSEMBLE), and convolutional neural networks (CNNs). The CNNs in particular attempt to increase overall classification accuracy by taking into account the shape of the plastic particles. In addition, the automatic optimization of the hyperparameters of the classification algorithms by the random search algorithm was investigated. The aim was to increase the accuracy of the classification models. About 400 particles each of 14 plastics from 12 plastic classes were examined. An attempt was made to train an overall model for the classification of all 12 plastics. The CNNs achieved the highest overall classification accuracy with 93.5%. Another attempt was made to classify 41 mixtures of industrially relevant plastics with a maximum of three plastic classes per mixture. The same average classification accuracy of 99.0% was achieved for the ENSEMBLE, SVM, and CNN algorithms. The target overall classification accuracy of 99.9% was achieved for 18 of the 41 compounds. The results show that the method presented is a promising approach for sorting black technical plastic waste.
The Application of LiDAR Data for the Solar Potential Analysis Based on Urban 3D Model
Authors: Prieto Iñaki , Izkara Jose Luis , Usobiaga Elena
Published: October 2019

Solar maps are becoming a popular resource and are available via the web to help plan investments for the benefits of renewable energy. These maps are especially useful when the results have high accuracy. LiDAR technology currently offers high-resolution data sources that are very suitable for obtaining an urban 3D geometry with high precision. Three-dimensional visualization also offers a more accurate and intuitive perspective of reality than 2D maps. This paper presents a new method for the calculation and visualization of the solar potential of building roofs on an urban 3D model, based on LiDAR data. The paper describes the proposed methodology to (1) calculate the solar potential, (2) generate an urban 3D model, (3) semantize the urban 3D model with different existing and calculated data, and (4) visualize the urban 3D model in a 3D web environment. The urban 3D model is based on the CityGML standard, which offers the ability to consistently combine geometry and semantics and enable the integration of different levels (building and city) in a continuous model. The paper presents the workflow and results of application to the city of Vitoria-Gasteiz in Spain. This paper also shows the potential use of LiDAR data in different domains that can be connected using different technologies and different scales.
LAM: Remote Sensing Image Captioning with Label-Attention Mechanism
Authors: Zhang Zhengyuan , Diao Wenhui , Zhang Wenkai , Yan Menglong , Gao Xin , Sun Xian
Published: October 2019

Significant progress has been made in remote sensing image captioning by encoder-decoder frameworks. The conventional attention mechanism is prevalent in this task but still has some drawbacks. The conventional attention mechanism only uses visual information about the remote sensing images without considering using the label information to guide the calculation of attention masks. To this end, a novel attention mechanism, namely Label-Attention Mechanism (LAM), is proposed in this paper. LAM additionally utilizes the label information of high-resolution remote sensing images to generate natural sentences to describe the given images. It is worth noting that, instead of high-level image features, the predicted categories’ word embedding vectors are adopted to guide the calculation of attention masks. Representing the content of images in the form of word embedding vectors can filter out redundant image features. In addition, it can also preserve pure and useful information for generating complete sentences. The experimental results from UCM-Captions, Sydney-Captions and RSICD demonstrate that LAM can improve the model’s performance for describing high-resolution remote sensing images and obtain better S m scores compared with other methods. S m score is a hybrid scoring method derived from the AI Challenge 2017 scoring method. In addition, the validity of LAM is verified by the experiment of using true labels.
A Compensation Method for a Time–Space Variant Atmospheric Phase Applied to Time-Series GB-SAR Images
Authors: Hu Cheng , Deng Yunkai , Tian Weiming , Zhao Zheng
Published: October 2019

An atmospheric effect is a main error source that affects interferometric measurements. When a ground-based multiple-input multiple-output (GB-MIMO) radar, i.e., a specific type of GBsynthetic aperture radar (GB-SAR), was utilized to continuously monitor an open-pit mine, the interferometric phases of some interferograms were complexly space-variant due to time-variant weather conditions. The conventional method of atmospheric phase (AP) compensation was no longer applicable. This paper proposes an improved compensation method of a time-space variant AP applied to time-series GB-SAR images. The permanent scatterers (PSs) were classified into three types based on their different spatial properties: The noise-dominant PS (NPS), the deformationdominant PS (DPS), and the atmospheric effect-dominant PS (APS). The NPSs were firstly rejected based on the differential phase analysis of neighboring PSs. The DPSs were then rejected based on the cluster partition and selection. With the APSs, the space-variant AP was estimated with a spatial interpolation. To validate the feasibility of the proposed method, short-term and long-term experimental datasets were processed. Comparisons with a conventional method proved that the proposed method can well reduce AP errors and avoid the misunderstanding of motional areas.
Earthquake-Induced Landslide Mapping for the 2018 Hokkaido Eastern Iburi Earthquake Using PALSAR-2 Data
Authors: Aimaiti Yusupujiang , Liu Wen , Yamazaki Fumio , Maruyama Yoshihisa
Published: October 2019

Timely information about landslides during or immediately after an event is an invaluable source for emergency response and management. Using an active sensor, synthetic aperture radar (SAR) can capture images of the earth’s surface regardless of weather conditions and may provide a solution to the problem of mapping landslides when clouds obstruct optical imaging. The 2018 Hokkaido Eastern Iburi earthquake (Mw 6.6) and its aftershocks not only caused major damage with severe loss of life and property but also induced many landslides across the area. To gain a better understanding of the landslides induced by this earthquake, we proposed a method of landslide mapping using pre- and post-event Advanced Land Observation Satellite 2 Phased Array L-band Synthetic Aperture Radar 2 (ALOS-2 PALSAR-2) images acquired from both descending and ascending orbits. Moreover, the accuracy of the classification results was verified by comparisons with high-resolution optical images, and ground truth data (provided by GSI, Japan). The detected landslides show a good match with the reference optical images by visual comparison. The quantitative comparison results showed that a combination of the descending and ascending intensity-based landslide classification had the best accuracy with an overall accuracy and kappa coefficient of 80.1% and 0.45, respectively.
An Offline Coarse-To-Fine Precision Optimization Algorithm for 3D Laser SLAM Point Cloud
Authors: Dai Jicheng , Yan Li , Liu Hua , Chen Changjun , Huo Liang
Published: October 2019

3D laser simultaneous localization and mapping (SLAM) technology is one of the most efficient methods to capture spatial information. However, the low-precision of 3D laser SLAM point cloud limits its application in many fields. In order to improve the precision of 3D laser SLAM point cloud, we presented an offline coarse-to-fine precision optimization algorithm. The point clouds are first segmented and registered at the local level. Then, a pose graph of point cloud segments is constructed using feature similarity and global registration. At last, all segments are aligned and merged into the final optimized result. In addition, a cycle based error edge elimination method is utilized to guarantee the consistency of the pose graph. The experimental results demonstrated that our algorithm achieved good performance both in our test datasets and the Cartographer public dataset. Compared with the reference data obtained by terrestrial laser scanning (TLS), the average point-to-point distance root mean square errors (RMSE) of point clouds generated by Google’s Cartographer and LOAM laser SLAM algorithms are reduced by 47.3% and 53.4% respectively after optimization in our datasets. And the average plane-to-plane distances of them are reduced by 50.9% and 52.1% respectively.
Estimating Gravimetric Water Content of a Winter Wheat Field from L-Band Vegetation Optical Depth
Authors: Meyer Thomas , Jagdhuber Thomas , Piles María , Fink Anita , Grant Jennifer , Vereecken Harry , Jonard François
Published: October 2019

A considerable amount of water is stored in vegetation, especially in regions with high precipitation rates. Knowledge of the vegetation water status is essential to monitor changes in ecosystem health and to assess the vegetation influence on the water budget. In this study, we develop and validate an approach to estimate the gravimetric vegetation water content (mg), defined as the amount of water [kg] per wet biomass [kg], based on the attenuation of microwave radiation through vegetation. mg is expected to be more closely related to the actual water status of a plant than the area-based vegetation water content (VWC), which expresses the amount of water [kg] per unit area [m2]. We conducted the study at the field scale over an entire growth cycle of a winter wheat field. Tower-based L-band microwave measurements together with in situ measurements of vegetation properties (i.e., vegetation height, and mg for validation) were performed. The results indicated a strong agreement between the in situ measured and retrieved mg (R2 of 0.89), with mean and standard deviation (STD) values of 0.55 and 0.26 for the in situ measured mg and 0.57 and 0.19 for the retrieved mg, respectively. Phenological changes in crop water content were captured, with the highest values of mg obtained during the growth phase of the vegetation (i.e., when the water content of the plants and the biomass were increasing) and the lowest values when the vegetation turned fully senescent (i.e., when the water content of the plant was the lowest). Comparing in situ measured mg and VWC, we found their highest agreement with an R2 of 0.95 after flowering (i.e., when the vegetation started to lose water) and their main differences with an R2 of 0.21 during the vegetative growth of the wheat vegetation (i.e., where the mg was constant and VWC increased due to structural changes in vegetation). In addition, we performed a sensitivity analysis on the vegetation volume fraction (δ), an input parameter to the proposed approach which represents the volume percentage of solid plant material in air. This δ-parameter is shown to have a distinct impact on the thermal emission at L-band, but keeping δ constant during the growth cycle of the winter wheat appeared to be valid for these mg retrievals.
Estimating Fine-Scale Heat Vulnerability in Beijing Through Two Approaches: Spatial Patterns, Similarities, and Divergence
Authors: Guo Xuan , Huang Ganlin , Jia Peng , Wu Jianguo
Published: October 2019

High temperatures in urban areas cause a significant negative impact on the residents’ health. In a megacity such as Beijing, where both the land cover and social composition of residents are highly spatially heterogeneous, understanding heat vulnerability at a relatively fine scale is a prerequisite for place-based heat intervention actions. Both principal component analysis (PCA) and equal-weighted index (EWI) are commonly used in heat vulnerability studies. However, the extent to which the choice of these approaches may impact the results remains unclear. Our study aimed to fill this gap by estimating heat vulnerability at the jiedao scale (the smallest census unit) in Beijing based on socioeconomic characteristics, heat exposure, and the use of air conditioners. Our results show that the choice of methods had a considerable impact on the spatial patterns of estimated heat vulnerability. PCA resulted in a ring-like pattern (high in the central and low in the suburb), whereas EWI revealed a north–south discrepancy (low in the north and high in the south). Such a difference is caused by the weighting scheme used in the PCA. Our findings indicate that heat vulnerability pattern revealed by a single measure needs to be interpreted with caution because different measures may produce disparate results.
The Impact of EU Grants Absorption on Land Cover Changes—The Case of Poland
Authors: Leśniewska-Napierała Katarzyna , Nalej Marta , Napierała Tomasz
Published: October 2019

The main goal of the paper is to verify the impact of the absorption of European Union (EU) grants on land cover changes in Polish municipalities in the years 2012–2018. The selection of the research area was justified by the fact that Poland is the largest and significantly spatially differentiated transition economy in Central-Eastern Europe, recognised as a substantial beneficiary of EU accession in 2004. The time range of analysis was set as the result of a comparison of data availability in Corine Land Cover (CLC) and Statistics Poland. The CLC dataset referring to land cover and land use changes between 2012 and 2018 was used. The focus on modifications taking place within one of the main land cover groups at level 3 of detail was applied in this research. These changes were analysed as percentages referring to the area of the municipality and to the total area of changes in the investigated period. Two categories of EU grants were considered: total and infrastructural (granted under EU Operational Programme “Infrastructure and Environment”). Moreover, some control economic, social, demographic, institutional, infrastructural, and environmental variables were applied to better explain land cover changes. Moran’s local statistic was employed to detect spatial hot-spots of EU grants absorption, as well as hot-spots of land cover changes. Then, a collection of various variables related to determinants of land cover changes was set. Economic factors, including EU grants absorption, as well as factors related to accessibility, agrarian structure, demography, environment, and spatial planning were investigated. Principal Component Analysis (PCA) was employed to convert the set of all considered variables into a set of few uncorrelated predictors. Finally, Geographically Weighted Regression (GWR) was applied to describe the spatially varied impact of investigated determinants, including EU grants, represented by estimated principal components on land cover changes.
A Mobile Anchor Node Assisted RSSI Localization Scheme in Underwater Wireless Sensor Networks
Authors: Sun Yanlong , Yuan Yazhou , Xu Qimin , Hua Changchun , Guan Xinping
Published: October 2019

In this paper, a mobile anchor node assisted RSSI localization scheme in underwater wireless sensor networks (UWSNs) is proposed, which aims to improve location accuracy and shorten location time. First, to improve location accuracy, we design a support vector regression (SVR) based interpolation method to estimate the projection of sensor nodes on the linear trajectory of the mobile anchor node. The proposed method increases the accuracy of the nonlinear regression model of noisy measured data and synchronously decreases the estimation error caused by the discreteness of measured data. Second, to shorten location time, we develop a curve matching method to obtain the perpendicular distance from sensor nodes to the linear trajectory of the mobile anchor node. The location of the sensor node can be calculated based on the projection and the perpendicular distance. Compared with existing schemes that require the anchor node to travel at least two trajectories, the proposed scheme only needs one-time trajectory to locate sensor nodes, and the location time is shortened with the reduction in the number of trajectories. Finally, simulation results prove that the proposed scheme can obtain more accurate sensor node location in less time compared with the existing schemes.
Traversability Assessment and Trajectory Planning of Unmanned Ground Vehicles with Suspension Systems on Rough Terrain
Authors: Zhang Kai , Yang Yi , Fu Mengyin , Wang Meiling
Published: October 2019

This paper presents a traversability assessment method and a trajectory planning method. They are key features for the navigation of an unmanned ground vehicle (UGV) in a non-planar environment. In this work, a 3D light detection and ranging (LiDAR) sensor is used to obtain the geometric information about a rough terrain surface. For a given SE(2) pose of the vehicle and a specific vehicle model, the SE(3) pose of the vehicle is estimated based on LiDAR points, and then a traversability is computed. The traversability tells the vehicle the effects of its interaction with the rough terrain. Note that the traversability is computed on demand during trajectory planning, so there is not any explicit terrain discretization. The proposed trajectory planner finds an initial path through the non-holonomic A*, which is a modified form of the conventional A* planner. A path is a sequence of poses without timestamps. Then, the initial path is optimized in terms of the traversability, using the method of Lagrange multipliers. The optimization accounts for the model of the vehicle’s suspension system. Therefore, the optimized trajectory is dynamically feasible, and the trajectory tracking error is small. The proposed methods were tested in both the simulation and the real-world experiments. The simulation experiments were conducted in a simulator called Gazebo, which uses a physics engine to compute the vehicle motion. The experiments were conducted in various non-planar experiments. The results indicate that the proposed methods could accurately estimate the SE(3) pose of the vehicle. Besides, the trajectory cost of the proposed planner was lower than the trajectory costs of other state-of-the-art trajectory planners.
An Ultra-Short Baseline Positioning Model Based on Rotating Array & Reusing Elements and Its Error Analysis
Authors: Tong Jinwu , Xu Xiaosu , Hou Lanhua , Li Yao , Wang Jian , Zhang Liang
Published: October 2019

The USBL (Ultra-Short Base Line) positioning system is widely used in underwater acoustic positioning systems due to its small size and ease of use. The traditional USBL positioning system is based on ‘slant range and azimuth’. The positioning error is an increasing function with the increase in distance and the positioning accuracy depends on the ranging accuracy of the underwater target. This method is not suitable for long-distance underwater positioning operations. This paper proposes a USBL positioning calculation model based on depth information for ‘rotating array and reusing elements’. This method does not need to measure the distance between the USBL acoustic array and target, so it can completely eliminate the influence of long-distance ranging errors in USBL positioning. The theoretical analysis and simulation experiments show that the new USBL positioning model based on ‘rotating array and reusing elements’ can completely eliminate the influence of the wavelength error and spacing error of underwater acoustic signals on the positioning accuracy of USBL. The positioning accuracy can be improved by approximately 90%, and the horizontal positioning error within a positioning distance of 1000 m is less than 1.2 m. The positioning method has high precision performance in the long distance, and provides a new idea for the engineering design of a USBL underwater positioning system.
RMLNet—A Reliable Wireless Network for a Multiarea TDOA-Based Localization System
Authors: Xue Yuan , Su Wei , Yang Dong , Wang Hongchao , Zhang Weiting
Published: October 2019

Ultrawideband (UWB) wireless communication is a promising spread-spectrum technology for accurate localization among devices characterized by a low transmission power, a high rate and immunity to multipath propagation. The accurately of the clock synchronization algorithm and the time-difference-of-arrival (TDOA) localization algorithm provide precise position information of mobile nodes with centimeter-level accuracy for the UWB localization system. However, the reliability of target node localization for multi-area localization remains a subject of research. Especially for dynamic and harsh indoor environments, an effective scheme among competing target nodes for localization due to the scarcity of radio resources remains a challenge. In this paper, we present RMLNet, an approach focus on the medium access control (MAC) layer, which guarantees general localization application reliability on multi-area localization. Specifically, the design requires specific and optimized solutions for managing and coordinating multiple anchor nodes. In addition, an approach for target area determination is proposed, which can approximately determine the region of the target node by the received signal strength indication (RSSI), to support RMLNet. Furthermore, we implement the system to estimate the localization of the target node and evaluate its performance in practice. Experiments and simulations show that RMLNet can achieve localization application reliability multi-area localization with a better localization performance of competing target nodes.
Vision Based Modeling of Plants Phenotyping in Vertical Farming under Artificial Lighting
Authors: Franchetti Benjamin , Ntouskos Valsamis , Giuliani Pierluigi , Herman Tiara , Barnes Luke , Pirri Fiora
Published: October 2019

In this paper, we present a novel method for vision based plants phenotyping in indoor vertical farming under artificial lighting. The method combines 3D plants modeling and deep segmentation of the higher leaves, during a period of 25–30 days, related to their growth. The novelty of our approach is in providing 3D reconstruction, leaf segmentation, geometric surface modeling, and deep network estimation for weight prediction to effectively measure plant growth, under three relevant phenotype features: height, weight and leaf area. Together with the vision based measurements, to verify the soundness of our proposed method, we also harvested the plants at specific time periods to take manual measurements, collecting a great amount of data. In particular, we manually collected 2592 data points related to the plant phenotype and 1728 images of the plants. This allowed us to show with a good number of experiments that the vision based methods ensure a quite accurate prediction of the considered features, providing a way to predict plant behavior, under specific conditions, without any need to resort to human measurements.
Analysis of Agile Canine Gait Characteristics Using Accelerometry
Authors: Hayati Hasti , Mahdavi Fatemeh , Eager David
Published: October 2019

The high rate of severe injuries associated with racing greyhounds poses a significant problem for both animal welfare and the racing industry. Using accelerometry to develop a better understanding of the complex gait of these agile canines may help to eliminate injury contributing factors. This study used a single Inertial Measurement Unit (IMU) equipped with a tri-axial accelerometer to characterise the galloping of thirty-one greyhounds on five different race tracks. The dorsal-ventral and anterior-posterior accelerations were analysed in both the time and frequency domains. The fast Fourier transform (FFT) and Morlet wavelet transform were applied to signals. The time-domain signals were synced with the corresponding high frame rate videos of the race. It was observed that the acceleration peaks in the dorsal-ventral accelerations correspond to the hind-leg strikes which were noted to be fifteen times the greyhound’s weight. The FFT analysis showed that the stride frequencies in all tracks were around 3.5 Hz. The Morlet wavelet analysis also showed a reduction in both the frequency and magnitude of signals, which suggests a speed reduction throughout the race. Also, by detecting abrupt changes along the track, the wavelet analysis highlighted potentially hazardous locations on the track. In conclusion, the methods applied in this research contribute to animal safety and welfare by eliminating the factors leading to injuries through optimising the track design and surface type.
A Genetic-Based Extreme Gradient Boosting Model for Detecting Intrusions in Wireless Sensor Networks
Authors: Alqahtani Mnahi , Gumaei Abdu , Mathkour Hassan , Maher Ben Ismail Mohamed
Published: October 2019

An Intrusion detection system is an essential security tool for protecting services and infrastructures of wireless sensor networks from unseen and unpredictable attacks. Few works of machine learning have been proposed for intrusion detection in wireless sensor networks and that have achieved reasonable results. However, these works still need to be more accurate and efficient against imbalanced data problems in network traffic. In this paper, we proposed a new model to detect intrusion attacks based on a genetic algorithm and an extreme gradient boosting (XGBoot) classifier, called GXGBoost model. The latter is a gradient boosting model designed for improving the performance of traditional models to detect minority classes of attacks in the highly imbalanced data traffic of wireless sensor networks. A set of experiments were conducted on wireless sensor network-detection system (WSN-DS) dataset using holdout and 10 fold cross validation techniques. The results of 10 fold cross validation tests revealed that the proposed approach outperformed the state-of-the-art approaches and other ensemble learning classifiers with high detection rates of 98.2%, 92.9%, 98.9%, and 99.5% for flooding, scheduling, grayhole, and blackhole attacks, respectively, in addition to 99.9% for normal traffic.
Correction: Screening and Biosensor-Based Approaches for Lung Cancer Detection. Sensors 2017, 17, 2420
Authors: Wang Lulu
Published: October 2019

The authors wish to make the following corrections to this paper [...]
An Overview of High Frequency Acoustic Sensors—QCMs, SAWs and FBARs—Chemical and Biochemical Applications
Authors: Mujahid Adnan , Afzal Adeel , Dickert Franz L.
Published: October 2019

Acoustic devices have found wide applications in chemical and biosensing fields owing to their high sensitivity, ruggedness, miniaturized design and integration ability with on-field electronic systems. One of the potential advantages of using these devices are their label-free detection mechanism since mass is the fundamental property of any target analyte which is monitored by these devices. Herein, we provide a concise overview of high frequency acoustic transducers such as quartz crystal microbalance (QCM), surface acoustic wave (SAW) and film bulk acoustic resonators (FBARs) to compare their working principles, resonance frequencies, selection of piezoelectric materials for their fabrication, temperature-frequency dependency and operation in the liquid phase. The selected sensor applications of these high frequency acoustic transducers are discussed primarily focusing on the two main sensing domains, i.e., biosensing for working in liquids and gas/vapor phase sensing. Furthermore, the sensor performance of high frequency acoustic transducers in selected cases is compared with well-established analytical tools such as liquid chromatography mass spectrometry (LC-MS), gas chromatographic (GC) analysis and enzyme-linked immunosorbent assay (ELISA) methods. Finally, a general comparison of these acoustic devices is conducted to discuss their strengths, limitations, and commercial adaptability thus, to select the most suitable transducer for a particular chemical/biochemical sensing domain.
A Hybrid Spectrum Access Strategy with Channel Bonding and Classified Secondary User Mechanism in Multichannel Cognitive Radio Networks
Authors: Zhao Yuan , Peng Minglei , Liu Jiemin
Published: October 2019

Cognitive radio networks (CRNs) can improve spectrum utilization by allowing secondary users (SUs) to dynamically access channels unoccupied by primary users (PUs). The spectrum access strategy, as a point to enhance user performance, has received much attention. In this paper, we propose a hybrid access mode for network users in multichannel CRNs. For meeting different SU demands, SUs are classified as SU1s and SU2s. We further introduce a channel bonding scheme for high-priority (PU and SU1) user packets to enhance transmission efficiency. At the same time, we propose a hybrid spectrum access strategy for SU2 packets to improve their transmission stability. By establishing a Markov chain model, some important SU2 packets’ performance measures are derived. Furthermore, we display the comparison of hybrid, overlay and underlay modes by numerical results to analyze the advantages of different modes.