bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimization of Waterborne Poly(Urethane-Acrylate) Nanoemulsions Based on Cationic Polymerizable Macrosurfactants with Different Hydrophobic Side Chain Length
Authors: Fei Guiqiang , Geng Huanqiong , Wang Haihua , Liu Xuan , Liao Yong , Shao Yanming , Wang Mengxi
Published: November 2019

In situ surfactant-free emulsion polymerization can help avoid the utilization of harmful co-solvents and surfactants in the preparation of waterborne poly(urethane-acrylate) (WPUA) nanoemulsion, but the solid content is extremely limited, which will affect the drying rate and film-forming properties. The utilization of polymerizable macrosurfactants can overcome the above problems. However, the research on cationic polymerizable macrosurfactants is extremely scarce. In this work, cationic dimethylaminoethyl methacrylate-b-alkyl methacrylates block copolymers (PDM-b-PRMA) with terminal double bonds and different hydrophobic side chain (HSC) lengths were fabricated via catalytic chain transfer polymerization (CCTP). HSC length of PDM-b-PRMA played an important role in the phase inversion, morphology, rheological behavior of WPUA nanoemulsions, as well as the comprehensive performance of WPUA/PDM-b-PRMA films. Polymerizable PDM-b-PBMA macrosurfactant had smaller molecular weight, lower surface tension and colloidal size than the random copolymer (PDM-co-PBMA) by traditional free radical polymerization. It was easy for PDM-b-PRMA to orientedly assemble at the oil/water interface and provide better emulsifying ability when the carbon number of HSC was four. Compared with WPUA/PDM-co-PBMA, WPUA/PDM-b-PBMA had a smaller particle size, stability and better film-forming properties. This work elucidated the mechanisms of HSC length in the fabrication of cationic PDM-b-PRMA and provides a novel strategy to prepare cationic WPUA of high performance.
Nanocrystal Encapsulation, Release and Application Based on pH-Sensitive Covalent Dynamic Hyperbranched Polymers
Authors: Shi Yunfeng , Lei Gaiying , Zhou Linzhu , Li Yueyang , Zhang Xiaoming , Yang Yujiao , Peng Han , Peng Rui , Wang Huichun , Cai Xiufen , Chen Xinglong , Wang Mengyue , Wang Gang
Published: November 2019

A new strategy for nanocrystal encapsulation, release and application based on pH-sensitive covalent dynamic hyperbranched polymers is described. The covalent dynamic hyperbranched polymers, with multi-arm hydrophobic chains and a hydrophilic hyperbranched poly(amidoamine) (HPAMAM) core connected with pH-sensitive imine bonds (HPAMAM–DA), could encapsulate CdTe quantum dots (QDs) and Au nanoparticles (NPs). Benefiting from its pH response property, CdTe QDs and Au NPs encapsulated by HPAMAM–DA could be released to aqueous phase after imine hydrolysis. The released CdTe/HPAMAM and Au/HPAMAM nanocomposites exhibited excellent biological imaging behavior and high catalytic activities on p-nitrophenol hydrogenation, respectively.
Improvement of Rice Husk/HDPE Bio-Composites Interfacial Properties by Silane Coupling Agent and Compatibilizer Complementary Modification
Authors: Sun Jingmeng , Pang Yao , Yang Yingni , Zhao Junqi , Xia Rongqi , Li Yanchen , Liu Yi , Guo Hongwu
Published: November 2019

Composites using agricultural and forestry residues as raw materials with potentially high-performance, multifunctional and biodegradable ecological advantages, are viewed as very promising for new-generation lightweight and low-cost bio-based sustainable building materials. At present, the research on wood-plastic composite materials is relatively mature. However, it is still a challenge to effectively use other biomass and improve the interface of the high-polymer compound system. Herein, we proposed a simple and effective method to enhance the interfacial adhesion properties of rice husk fibre and High Density Polyethylene (HDPE) composites by the silane coupling agent KH-550 and compatibilizer Maleic anhydride grafted polyethylene (MAPE) with complementary modification. It was found that the coupling agent KH-550 cross-linked with the hydroxyl group on the husk fibre surface and solidified with the high polymer by –NH–, –C=O– functional group generation. Compatibilizer MAPE strengthened the two phases by covalently bonding with an ester linkage and lowered the roughness of the cross-section of the composites. Meanwhile the modification enhanced the dispersibility, and mechanical properties of the husk-high polymer compound system, the bending and flexural strength were improved by 11.5% and 28.9% with KH-550, and MAPE added, respectively. The flexural strength of the composites increased by 40.7% after complementary modification. Furthermore, the complementary modification treatment reduced the hydrophilic hydroxyl groups and increased the molecular chain to improve the water-resistance, elastic modulus and toughness of the composite. This study prepared a bio-composite, which is expected to expand the use of agricultural and forestry residues as an extension of wood-plastic composites.
Introduction to the Special Issue “Exploring the Future of Christian Monasticisms”
Authors: Peters Greg
Published: November 2019

The origins of Christian monasticism are buried deep in the shadows of Christian history, but without doubt it came to full fruition during the fourth century and continued to grow nearly unabated for the next millennium and a half [...]
Prediction of Competition Indices in a Norway Spruce and Silver Fir-Dominated Forest Using Lidar Data
Authors: Versace Soraya , Gianelle Damiano , Frizzera Lorenzo , Tognetti Roberto , Garfì Vittorio , Dalponte Michele
Published: November 2019

Competitive interactions are important predictors of tree growth. Spatial and temporal changes in resource availability, and variation in species and spatial patterning of trees alter competitive interactions, thus affecting tree growth and, hence, biomass. Competition indices are used to quantify the level of competition among trees. As these indices are normally computed only over small areas, where field measurements are done, it would be useful to have a tool to predict them over large areas. On this regard, remote sensing, and in particular light detection and ranging (lidar) data, could be the perfect tool. The objective of this study was to use lidar metrics to predict competition (on the basis of distance-dependent competition indices) of individual trees and to relate them with tree aboveground biomass (AGB). The selected study area was a mountain forest area located in the Italian Alps. The analyses focused on the two dominant species of the area: Silver fir (Abies alba Mill.) and Norway spruce (Picea abies (L.) H. Karst). The results showed that lidar metrics could be used to predict competition indices of individual trees (R2 above 0.66). Moreover, AGB decreased as competition increased, suggesting that variations in the availability of resources in the soil, and the ability of plants to withstand competition for light may influence the partitioning of biomass.
General External Uncertainty Models of Three-Plane Intersection Point for 3D Absolute Accuracy Assessment of Lidar Point Cloud
Authors: Kim Minsu , Park Seonkyung , Danielson Jeffrey , Irwin Jeffrey , Stensaas Gregory , Stoker Jason , Nimetz Joshua
Published: November 2019

The traditional practice to assess accuracy in lidar data involves calculating RMSEz (root mean square error of the vertical component). Accuracy assessment of lidar point clouds in full 3D (three dimension) is not routinely performed. The main challenge in assessing accuracy in full 3D is how to identify a conjugate point of a ground-surveyed checkpoint in the lidar point cloud with the smallest possible uncertainty value. Relatively coarse point-spacing in airborne lidar data makes it challenging to determine a conjugate point accurately. As a result, a substantial unwanted error is added to the inherent positional uncertainty of the lidar data. Unless we keep this additional error small enough, the 3D accuracy assessment result will not properly represent the inherent uncertainty. We call this added error “external uncertainty,” which is associated with conjugate point identification. This research developed a general external uncertainty model using three-plane intersections and accounts for several factors (sensor precision, feature dimension, and point density). This method can be used for lidar point cloud data from a wide range of sensor qualities, point densities, and sizes of the features of interest. The external uncertainty model was derived as a semi-analytical function that takes the number of points on a plane as an input. It is a normalized general function that can be scaled by smooth surface precision (SSP) of a lidar system. This general uncertainty model provides a quantitative guideline on the required conditions for the conjugate point based on the geometric features. Applications of the external uncertainty model were demonstrated using various lidar point cloud data from the U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) library to determine the valid conditions for a conjugate point from three-plane modeling.
Differentially Deep Subspace Representation for Unsupervised Change Detection of SAR Images
Authors: Luo Bin , Hu Chudi , Su Xin , Wang Yajun
Published: November 2019

Temporal analysis of synthetic aperture radar (SAR) time series is a basic and significant issue in the remote sensing field. Change detection as well as other interpretation tasks of SAR images always involves non-linear/non-convex problems. Complex (non-linear) change criteria or models have thus been proposed for SAR images, instead of direct difference (e.g., change vector analysis) with/without linear transform (e.g., Principal Component Analysis, Slow Feature Analysis) used in optical image change detection. In this paper, inspired by the powerful deep learning techniques, we present a deep autoencoder (AE) based non-linear subspace representation for unsupervised change detection with multi-temporal SAR images. The proposed architecture is built upon an autoencoder-like (AE-like) network, which non-linearly maps the input SAR data into a latent space. Unlike normal AE networks, a self-expressive layer performing like principal component analysis (PCA) is added between the encoder and the decoder, which further transforms the mapped SAR data to mutually orthogonal subspaces. To make the proposed architecture more efficient at change detection tasks, the parameters are trained to minimize the representation difference of unchanged pixels in the deep subspace. Thus, the proposed architecture is namely the Differentially Deep Subspace Representation (DDSR) network for multi-temporal SAR images change detection. Experimental results on real datasets validate the effectiveness and superiority of the proposed architecture.
A Review of Regional and Global Gridded Forest Biomass Datasets
Authors: Zhang Yuzhen , Liang Shunlin , Yang Lu
Published: November 2019

Forest biomass quantification is essential to the global carbon cycle and climate studies. Many studies have estimated forest biomass from a variety of data sources, and consequently generated some regional and global maps. However, these forest biomass maps are not well known and evaluated. In this paper, we reviewed an extensive list of currently available forest biomass maps. For each map, we briefly introduced the data sources, the algorithms used, and the associated uncertainties. Large-scale biomass datasets were compared across Europe, the conterminous United States, Southeast Asia, tropical Africa and South America. Results showed that these forest biomass datasets were almost entirely inconsistent, particularly in woody savannas and savannas across these regions. The uncertainties in biomass maps could be from a variety of sources including the chosen allometric equations used to calculate field data, the choice and quality of remotely sensed data, as well as the algorithms to map forest biomass or extrapolation techniques, but these uncertainties have not been fully quantified. We suggested the future directions for generating more accurate large-scale forest biomass maps should concentrate on the compilation of field biomass data, novel approaches of forest biomass mapping, and comprehensively addressing the accuracy of generated biomass maps.
Evaluation of Spaceborne GNSS-R Retrieved Ocean Surface Wind Speed with Multiple Datasets
Authors: Dong Zhounan , Jin Shuanggen
Published: November 2019

Spaceborne Global Navigation Satellite Systems-Reflectometry (GNSS-R) can estimate the geophysical parameters by receiving Earth’s surface reflected signals. The CYclone Global Navigation Satellite System (CYGNSS) mission with eight microsatellites launched by NASA in December 2016, which provides an unprecedented opportunity to rapidly acquire ocean surface wind speed globally. In this paper, a refined spaceborne GNSS-R sea surface wind speed retrieval algorithm is presented and validated with the ground surface reference wind speed from numerical weather prediction (NWP) and cross-calibrated multi-platform ocean surface wind vector analysis product (CCMP), respectively. The results show that when the wind speed was less than 20 m/s, the RMS of the GNSS-R retrieved wind could achieve 1.84 m/s in the case where the NWP winds were used as the ground truth winds, while the result was better than the NWP-based retrieved wind speed with an RMS of 1.68 m/s when the CCMP winds were used. The two sets of inversion results were further evaluated by the buoy winds, and the uncertainties from the NWP-derived and CCMP-derived model prediction wind speed were 1.91 m/s and 1.87 m/s, respectively. The accuracy of inversed wind speeds for different GNSS pseudo-random noise (PRN) satellites and types was also analyzed and presented, which showed similar for different PRN satellites and different types of satellites.
A Statistical Forest Reflectance Model
Authors: Kuusk Andres , Kuusk Joel , Lang Mait
Published: November 2019

The variability of forest reflectance among hemiboreal forests can be described with a few basis functions. Five basis functions describe almost 98% of variability of directional reflectance spectra in the optical spectral domain (400–1700 nm) in forest stands at the top of a canopy in nadir. A statistical forest reflectance model (SFRM) was developed, the input parameters of which are the forest parameters measured in the course of regular forest inventory. Nadir spectral reflectance of a forest stand is expressed in the SFRM as a linear combination of basis functions, the weights of which are linear combinations of the 15 stand parameters in the forest inventory database. Multiple correlations of the weights on the forest inventory parameters are determined separately for pine, spruce, and broadleaf forests. The basis functions are found from low altitude airborne measurements over managed forests in southeastern Estonia, where a forest management database is available. The model was validated against more than 3000 spectral signatures of forest stands from Sentinel-2 Multispectral Imager (MSI) measurements over a test site in southeastern Estonia. In most cases, the model predicts the forest reflectance spectrum at nadir with a relative error about 20–40%. The errors of reflectance values are less than 0.02 in most cases. The sole exception is the reflectance of broadleaf stands, which in near infrared bands of Sentinel-2 MSI is overestimated by 0.02–0.05.
Improving Forest Aboveground Biomass Estimation of Pinus densata Forest in Yunnan of Southwest China by Spatial Regression using Landsat 8 Images
Authors: Ou Guanglong , Lv Yanyu , Xu Hui , Wang Guangxing
Published: November 2019

Uncertainties in forest aboveground biomass (AGB) estimates resulting from over- and underestimations using remote sensing data have been widely studied. The uncertainties may occur due to the spatial effects of the plot data. In this study, we collected AGB data from a total of 147 Pinus densata forest sample plots in Yunnan of southwestern China and analyzed the spatial effects on the estimation of AGB. An ordinary least squares (OLS) and four spatial regression methods were compared for the estimation using Landsat 8-OLI images. Through the spatial analysis of AGB and residuals of model predictions, it was found that the spatial autocorrelation and heterogeneity of the plot data could not be ignored. Compared with the OLS, the impact of the spatial effects on AGB estimation could be reduced slightly by the spatial lag model (SLM) and the spatial error model (SEM) and greatly reduced by the linear mixed effects model (LMM) and geographically weighted regression (GWR) based on the distributions of prediction residuals, global Moran’s I, and Z score. The spatial regression models had better performance for model fitting and prediction because of the reduction in overestimations and underestimations for the forests with small and large AGB values, respectively. However, the reductions in the overestimations and underestimations varied depending on the spatial regression models. The GWR provided the most accurate predictions with the largest R2 (0.665), the smallest root mean square error (34.507), and mean relative error (−9.070%) by greatly reducing the AGB interval for overestimations occurring and significantly increasing the threshold of AGB from 150 Mg/ha to 200 Mg/ha for underestimations. Thus, GWR offered the greatest potential of improving the estimation of Pinus densata forest AGB in Yunnan of southwestern China.
Establishment of Plot-Yield Prediction Models in Soybean Breeding Programs Using UAV-Based Hyperspectral Remote Sensing
Authors: Zhang Xiaoyan , Zhao Jinming , Yang Guijun , Liu Jiangang , Cao Jiqiu , Li Chunyan , Zhao Xiaoqing , Gai Junyi
Published: November 2019

Yield evaluation of breeding lines is the key to successful release of cultivars, which is becoming a serious issue due to soil heterogeneity in enlarged field tests. This study aimed at establishing plot-yield prediction models using unmanned aerial vehicle (UAV)-based hyperspectral remote sensing for yield-selection in large-scale soybean breeding programs. Three sets of soybean breeding lines (1103 in total) were tested in blocks-in-replication experiments for plot yield and canopy spectral reflectance on 454~950 nm bands at different growth stages using a UAV-based hyperspectral spectrometer (Cubert UHD185 Firefly). The four elements for plot-yield prediction model construction were studied respectively and concluded as: the suitable reflectance-sampling unit-size in a plot was its 20%–80% central part; normalized difference vegetation index (NDVI) and ration vegetation index (RVI) were the best combination of vegetation indices; the initial seed-filling stage (R5) was the best for a single stage prediction, while another was the best combination for a two growth-stage prediction; and multi-variate linear regression was suitable for plot-yield prediction. In model establishment for each material-set, a random half was used for modelling and another half for verification. Twenty-one two growth-stage two vegetation-index prediction models were established and compared for their modelling coefficient of determination (RM2) and root mean square error of the model (RMSEM), verification RV2 and RMSEV, and their sum RS2 and RMSES. Integrated with the coincidence rate between the model predicted and the practical yield-selection results, the models, MA1-2, MA4-2 and MA6-2, with coincidence rates of 56.8%, 58.5% and 52.4%, respectively, were chosen for yield-prediction in yield-test nurseries. The established model construction elements and methods can be used as local models for pre-harvest yield-selection and post-harvest integrated yield-selection in advanced breeding nurseries as well as yield potential prediction in plant-derived-line nurseries. Furthermore, multiple models can be used jointly for plot-yield prediction in soybean breeding programs.
Application of UAV-Based Multi-angle Hyperspectral Remote Sensing in Fine Vegetation Classification
Authors: Yan Yanan , Deng Lei , Liu XianLin , Zhu Lin
Published: November 2019

To obtain a high-accuracy vegetation classification of high-resolution UAV images, in this paper, a multi-angle hyperspectral remote sensing system was built using a six-rotor UAV and a Cubert S185 frame hyperspectral sensor. The application of UAV-based multi-angle remote sensing in fine vegetation classification was studied by combining a bidirectional reflectance distribution function (BRDF) model for multi-angle remote sensing and object-oriented classification methods. This method can not only effectively reduce the classification phenomena that influence different objects with similar spectra, but also benefit the construction of a canopy-level BRDF. Then, the importance of the BRDF characteristic parameters are discussed in detail. The results show that the overall classification accuracy (OA) of the vertical observation reflectance based on BRDF extrapolation (BRDF_0°) (63.9%) was approximately 24% higher than that based on digital orthophoto maps (DOM) (39.8%), and kappa using BRDF_0° was 0.573, which was higher than that using DOM (0.301); a combination of the hot spot and dark spot features, as well as model features, improved the OA and kappa to around 77% and 0.720, respectively. The reflectance features near hot spots were more conducive to distinguishing maize, soybean, and weeds than features near dark spots; the classification results obtained by combining the observation principal plane (BRDF_PP) and on the cross-principal plane (BRDF_CP) features were best (OA = 89.2%, kappa = 0.870), and especially, this combination could improve the distinction among different leaf-shaped trees. BRDF_PP features performed better than BRDF_CP features. The observation angles in the backward reflection direction of the principal plane performed better than those in the forward direction. The observation angles associated with the zenith angles between −10° and −20° were most favorable for vegetation classification (solar position: zenith angle 28.86°, azimuth 169.07°) (OA was around 75%–80%, kappa was around 0.700–0.790); additionally, the most frequently selected bands in the classification included the blue band (466 nm–492 nm), green band (494 nm–570 nm), red band (642 nm–690 nm), red edge band (694 nm–774 nm), and the near-infrared band (810 nm–882 nm). Overall, the research results promote the application of multi-angle remote sensing technology in vegetation information extraction and provide important theoretical significance and application value for regional and global vegetation and ecological monitoring.
High-Sensitivity, Large Dynamic Range Refractive Index Measurement Using an Optical Microfiber Coupler
Authors: Wang Jiajia , Li Xiong , Fu Jun , Li Kaiwei
Published: November 2019

Wavelength tracking methods are widely employed in fiber-optic interferometers, but they suffer from the problem of fringe order ambiguity, which limits the dynamic range within half of the free spectral range. Here, we propose a new sensing strategy utilizing the unique property of the dispersion turning point in an optical microfiber coupler mode interferometer. Numerical calculations show that the position of the dispersion turning point is sensitive to the ambient refractive index, and its position can be approximated by the dual peaks/dips that lay symmetrically on both sides. In this study, we demonstrate the potential of this sensing strategy, achieving high sensitivities of larger than 5327.3 nm/RIU (refractive index unit) in the whole refractive index (RI) range of 1.333–1.4186. This sensor also shows good performance in narrow RI ranges with high resolution and high linearity. The resolution can be improved by increasing the length of the coupler.
Analysis and Adjustment of Positioning Error of PSD System for Mobile SOF-FTIR
Authors: Qu Liguo , Liu Jianguo , Deng Yasong , Xu Liang , Hu Kai , Yang Weifeng , Jin Ling , Cheng Xiaoxiao
Published: November 2019

A PSD-based solar spot position detection system is developed for solar tracking closed-loop control of mobile SOF-FTIR (Solar Occultation Flux method based on Fourier Transform Infrared spectrometer). The positioning error factors of PSD (position sensitive detector) are analyzed in detail. A voltage model for PSD signal conditioning circuit has been established to investigate the noise factors. The model shows that the positioning error is mainly related to PSD dark current and circuit gain. A static voltage deduction calibration method based on genetic algorithm is proposed to eliminate the effect of dark current. The gain ratio between channels is calculated based on the fitting curve slope of discrete position data of PSD center point with different light intensity for circuit gain calibration. The positioning accuracy and precision are greatly enhanced, especially when the light intensity is weak, compared with uncalibrated results. The positioning accuracy of center, middle and edge areas of PSD can reach 0.14%, 0.49%, and 1.09%, respectively, after correction in the range of light intensity voltage from 40 mV to 20 V. The corresponding standard deviations of each region are 0.005, 0.009, and 0.014, respectively. The adjustment methods proposed in this paper improve both measurement accuracy and detection limit. The results demonstrate that the calibrated PSD positioning accuracy can meet the requirements of SOF-FTIR for solar tracking.
Measurement of Pulse Wave Signals and Blood Pressure by a Plastic Optical Fiber FBG Sensor
Authors: Haseda Yuki , Bonefacino Julien , Tam Hwa-Yaw , Chino Shun , Koyama Shouhei , Ishizawa Hiroaki
Published: November 2019

Fiber Bragg grating (FBG) sensors fabricated in silica optical fiber (Silica-FBG) have been used to measure the strain of human arteries as pulse wave signals. A variety of vital signs including blood pressure can be derived from these signals. However, silica optical fiber presents a safety risk because it is easily fractured. In this research, an FBG sensor fabricated in plastic optical fiber (POF-FBG) was employed to resolve this problem. Pulse wave signals were measured by POF-FBG and silica-FBG sensors for four subjects. After signal processing, a calibration curve was constructed by partial least squares regression, then blood pressure was calculated from the calibration curve. As a result, the POF-FBG sensor could measure the pulse wave signals with an signal to noise (SN) ratio at least eight times higher than the silica-FBG sensor. Further, the measured signals were substantially similar to those of an acceleration plethysmograph (APG). Blood pressure is measured with low error, but the POF-FBG APG correlation is distributed from 0.54 to 0.72, which is not as high as desired. Based on these results, pulse wave signals should be measured under a wide range of reference blood pressures to confirm the reliability of blood pressure measurement uses POF-FBG sensors.
A Novel Method for Estimating Pitch and Yaw of Rotating Projectiles Based on Dynamic Constraints
Authors: An Liangliang , Wang Liangming , Liu Ning , Fu Jian , Zhong Yang
Published: November 2019

This paper addresses the difficult problem of measuring the attitude of a high-spinning projectile and presents a novel method for estimating the pitch and yaw angles of the projectile in flight. The method is based on analysis of the external moment of the rotating projectile during flight and theoretical derivations obtained from the dynamics’ equations. First, the principle of zero-crossing method is introduced, which explains the process of geomagnetic azimuth and roll measurements by the non-orthogonal geomagnetic sensor combination. Then, the dynamics constraint equations between the Euler angles and flight-path angle, trajectory deflection angle of the projectile are derived using the dynamics equations of the projectile rotating around the centroid, and analysis of the flight characteristics of the projectile in stable flight. Next, the spatial orientation relationship between pitch, yaw angles and magnetic azimuth is established based on the physical principle of geomagnetic azimuth. Finally, the pitch and yaw angles are estimated using the unscented Kalman filter (UKF), with the dynamics constraint equations serving as the driving equations. In the UKF prediction stage, the Runge-Kutta method is used to discretize the state equation that improves the prediction accuracy. Simulation results show that the proposed method can be used to accurately calculate the pitch and yaw angles, and results of experimental data processing also verify the feasibility of the proposed method for real-world applications.
Detection of the Strains Induced in Murine Tibias by Ex Vivo Uniaxial Loading with Different Sensors
Authors: Rizzuto Emanuele , Peruzzi Barbara , Giudice Mariagrazia , Urciuoli Enrica , Pittella Erika , Piuzzi Emanuele , Musarò Antonio , Del Prete Zaccaria
Published: November 2019

In this paper, the characterization of the main techniques and transducers employed to measure local and global strains induced by uniaxial loading of murine tibiae is presented. Micro strain gauges and digital image correlation (DIC) were tested to measure local strains, while a moving coil motor-based length transducer was employed to measure relative global shortening. Local strain is the crucial parameter to be measured when dealing with bone cell mechanotransduction, so we characterized these techniques in the experimental conditions known to activate cell mechanosensing in vivo. The experimental tests were performed using tibia samples excised from twenty-two C57BL/6 mice. To evaluate measurement repeatability we computed the standard deviation of ten repetitive compressions to the mean value. This value was lower than 3% for micro strain gauges, and in the range of 7%–10% for DIC and the length transducer. The coefficient of variation, i.e., the standard deviation to the mean value, was about 35% for strain gauges and the length transducer, and about 40% for DIC. These results provided a comprehensive characterization of three methodologies for local and global bone strain measurement, suggesting a possible field of application on the basis of their advantages and limitations.
A Novel Radar HRRP Recognition Method with Accelerated T-Distributed Stochastic Neighbor Embedding and Density-Based Clustering
Authors: Wu Hao , Dai Dahai , Wang Xuesong
Published: November 2019

High-resolution range profile (HRRP) has attracted intensive attention from radar community because it is easy to acquire and analyze. However, most of the conventional algorithms require the prior information of targets, and they cannot process a large number of samples in real time. In this paper, a novel HRRP recognition method is proposed to classify unlabeled samples automatically where the number of categories is unknown. Firstly, with the preprocessing of HRRPs, we adopt principal component analysis (PCA) for dimensionality reduction of data. Afterwards, t-distributed stochastic neighbor embedding (t-SNE) with Barnes–Hut approximation is conducted for the visualization of high-dimensional data. It proves to reduce the dimensionality, which has significantly improved the computation speed. Finally, it is exhibited that the recognition performance with density-based clustering is superior to conventional algorithms under the condition of large azimuth angle ranges and low signal-to-noise ratio (SNR).
Resource Allocation in Cognitive Radio Wireless Sensor Networks with Energy Harvesting
Authors: Xu Haitao , Gao Hongjie , Zhou Chengcheng , Duan Ruifeng , Zhou Xianwei
Published: November 2019

The progress of science and technology and the expansion of the Internet of Things make the information transmission between communication infrastructure and wireless sensors become more and more convenient. For the power-limited wireless sensors, the life time can be extended through the energy-harvesting technique. Additionally, wireless sensors can use the unauthored spectrum resource to complete certain information transmission tasks based on cognitive radio. Harvesting enough energy from the environments, the wireless sensors, works as the second users (SUs) can lease spectrum resource from the primary user (PU) to finish their task and bring additional transmission cost to themselves. To minimize the overall cost of SUs and to maximize the spectrum profit of the PU during the information transmission period, we formulated a differential game model to solve the resource allocation problem in the cognitive radio wireless sensor networks with energy harvesting, considering the SUs as the game players. By solving the proposed resource allocation game model, we found the open loop Nash equilibrium solutions and feedback Nash equilibrium solutions for all SUs as the optimal control strategies. Ultimately, series numerical simulation experiments have been made to demonstrate the rationality and effectiveness of the game model.
Machine Learning Techniques Applied to Dose Prediction in Computed Tomography Tests
Authors: Garcia-Sanchez Antonio-Javier , Garcia Angosto Enrique , Llor Jose Luis , Serna Berna Alfredo , Ramos David
Published: November 2019

Increasingly more patients exposed to radiation from computed axial tomography (CT) will have a greater risk of developing tumors or cancer that are caused by cell mutation in the future. A minor dose level would decrease the number of these possible cases. However, this framework can result in medical specialists (radiologists) not being able to detect anomalies or lesions. This work explores a way of addressing these concerns, achieving the reduction of unnecessary radiation without compromising the diagnosis. We contribute with a novel methodology in the CT area to predict the precise radiation that a patient should be given to accomplish this goal. Specifically, from a real dataset composed of the dose data of over fifty thousand patients that have been classified into standardized protocols (skull, abdomen, thorax, pelvis, etc.), we eliminate atypical information (outliers), to later generate regression curves employing diverse well-known Machine Learning techniques. As a result, we have chosen the best analytical technique per protocol; a selection that was thoroughly carried out according to traditional dosimetry parameters to accurately quantify the dose level that the radiologist should apply in each CT test.
Pose Prediction of Autonomous Full Tracked Vehicle Based on 3D Sensor
Authors: Ni Tao , Li Wenhang , Zhang Hongyan , Yang Haojie , Kong Zhifei
Published: November 2019

Autonomous vehicles can obtain real-time road information using 3D sensors. With road information, vehicles avoid obstacles through real-time path planning to improve their safety and stability. However, most of the research on driverless vehicles have been carried out on urban even driveways, with little consideration of uneven terrain. For an autonomous full tracked vehicle (FTV), the uneven terrain has a great impact on the stability and safety. In this paper, we proposed a method to predict the pose of the FTV based on accurate road elevation information obtained by 3D sensors. If we could predict the pose of the FTV traveling on uneven terrain, we would not only control the active suspension system but also change the driving trajectory to improve the safety and stability. In the first, 3D laser scanners were used to get real-time cloud data points of the terrain for extracting the elevation information of the terrain. Inertial measurement units (IMUs) and GPS are essential to get accurate attitude angle and position information. Then, the dynamics model of the FTV was established to calculate the vehicle’s pose. Finally, the Kalman filter was used to improve the accuracy of the predicted pose. Compared to the traditional method of driverless vehicles, the proposed approach was more suitable for autonomous FTV. The real-world experimental result demonstrated the accuracy and effectiveness of our approach.
A Novel Finger-Controlled Passive RFID Tag Design for Human–Machine Interaction
Authors: Liu Qi , Li Hui , Yu Yu-Feng , Zhao Wen-Sheng , Zhang Shuai
Published: November 2019

Radio frequency identification (RFID) has shown its potential in human–machine interaction thanks to its inherent function of identification and relevant physical information of signals, but complex data processing and undesirable input accuracy restrict its application and promotion in practical use. This paper proposes a novel finger-controlled passive RFID tag design for human–machine interaction. The tag antenna is based on a dipole antenna with a separated T-match structure, which is able to adjust the state of the tag by the press of a finger. The state of the proposed tag can be recognized directly by the code received by the RFID reader, and no complex data processing is needed. Since the code is hardly affected by surroundings, the proposed tag is suitable to be used as a wireless switch or control button in multiple scenarios. Moreover, arrays of the proposed tag with rational tag arrangements could contribute to a series of manual control devices, such as a wireless keyboard, a remote controller, and a wireless gamepad, without batteries. A 3 × 4 array of the finger-controlled tag is presented to constitute a simple passive RFID keyboard as an example of the applications of the proposed tag array and it refers to the arrangement of a keypad and can achieve precise, convenient, quick, and practical commands and text input into machines by pressing the tags with fingers. Simulations and measurements of the proposed tag and tag array have been carried out to validate their performances in human–machine interaction.
Culture and Art Education to Promote Cultural Welfare in Civil Society
Authors: Song Wonjin , Kim Boyoung
Published: November 2019

Today, intellectual education based on the various experiences and emotions of citizens through culture and art education draws attention in terms of obtaining improvements in social welfare. Citizens can become further aware of culture and art and get a wider range of benefits in cultural welfare. Culture and art education are important activities of cultural welfare. This study examines some of the successes of the Seoul Arts Center academy, which represents and hosts diverse cultural and art activities in South Korea. These activities have led to successful promotion of culture and art education for Seoul citizens. Based on Lewin’s change management process model, the cases that were analyzed focus on the change in culture and art education in the Seoul Arts Center academy. Findings from this study indicate the contribution to the improvement in cultural welfare for citizens from their engagement in activities within art education and culture. The discussion also highlights that good management of the changes in educational activity with the citizens’ needs and improvements in the maturity of civil society were critical factors for educational sustainability and success.
Comparative Review and Analysis of Organizational (In)Efficiency Indicators in Qatar
Authors: Al-Shaiba Abdulla S. , Al-Ghamdi Sami G. , Koc Muammer
Published: November 2019

Organization efficiency is a pivotal paradigm for an organization’s profitability in short-term cycles and long-term sustainability as a responsible stakeholder of a society. Measurement of organizational efficiency primarily depends on tracking the performance of the organization based on key performance indicators established commonly and differently by sectors. Literature on measuring organizational efficiency is limited in quantity and qualitatively poor, specifically on organizations operating in the Middle East and the Arabian Gulf where specific culture and traditions take precedence. This paper brings about a comparative review on organizational efficiency models, measurements, and indicators based on international best practices and conduct a comparative analysis from the Qatari organizational context. The methodology involved in measuring and benchmarking organizational sustainability identify the “as is” state of organizational sustainability and efficiency in the organization. Identifying the best sustainability practices across peer organizations and equipping organizations with the appropriate tools and skills to implement the identified best practices to fill the efficiency gap is essential. Defining and benchmarking indicators with peer organizations help the management to understand the “as is” state of the organization and is the impetus for the advancement of short- and long-term sustainability goals. The key performance indicators and focal themes for air transport, electricity–public utility, oil and gas, retail banking, and steel manufacturing are compared and identified.
Measuring Risks of Confirming Warehouse Financing from the Third Party Logistics Perspective
Authors: Wang Zhixin , Wang Yue
Published: November 2019

Confirmation warehouse financing is an important model in supply chain finance. This type of financing has special characteristics due to the existence of the reverse repurchase link, and it increases the risk commitment of the core enterprise at a certain level. Previous research on supply chain financial risk mostly settled in ‘all-industry, multi-model’, ignoring the special risks of single mode. To supplement the vacancies in the current research, the special risks of supply chain finance should be identified under a single model. On this basis, a measurement index system for confirmation warehouse financing risk is created. The article uses a Back Propagation (BP) neural network to build a Third Party Logistics (3PL) perspective of the risk measurement model for confirmation warehouse financing. The said network is combined with the 24 sets of actual cases from ZY Logistics. MATLAB is used to train the sample data. Results show that the absolute errors—0.042998, −0.011102, 0.020514 and 0.039448—between the training value and the predicted value are smaller than the preset error value. Among the 24 cases, high-risk businesses reached 41.7%, whereas low-risk businesses only accounted for 29.2%. The ZY enterprise confirms that warehouse financial business risk is high, and this situation should be revised. Research shows that the risk measurement indicator system has good risk prediction ability. This study establishes and verifies the rationality of the risk measurement index system and provides a reliable reference for 3PL risk aversion in supply chain finance.
Comparative Analysis between International Research Hotspots and National-Level Policy Keywords on Artificial Intelligence in China from 2009 to 2018
Authors: Gao Jie , Huang Xinping , Zhang Lili
Published: November 2019

In the last decade, artificial intelligence (AI) has undergone many important developments in China and has risen to the level of national strategy, which is closely related to the areas of research and policy promotion. The interactive relationship between the hotspots of China’s international AI research and its national-level policy keywords is the basis for further clarification and reference in academics and political circles. There has been very little research on the interaction between academic research and policy making. Understanding the relationship between the content of academic research and the content emphasized by actual operational policy will help scholars to better apply research to practice, and help decision-makers to manage effectively. Based on 3577 English publications about AI published by Chinese scholars in 2009–2018, and 262 Chinese national-level policy documents published during this period, this study carried out scientometric analysis and quantitative analysis of policy documents through the knowledge maps of AI international research hotspots in China and the co-occurrence maps of Chinese policy keywords, and conducted a comparative analysis that divided China’s AI development into three stages: the initial exploration stage, the steady rising stage, and the rapid development stage. The studies showed that in the initial exploration stage (2009–2012), research hotspots and policy keywords had a certain alienation relationship; in the steady rising stage (2013–2015), research hotspots focused more on cutting-edge technologies and policy keywords focused more on macro-guidance, and the relationship began to become close; and in the rapid development stage (2016–2018), the research hotspots and policy keywords became closely integrated, and they were mutually infiltrated and complementary, thus realizing organic integration and close connection. Through comparative analysis between international research hotspots and national-level policy keywords on AI in China from 2009 to 2018, the development of AI in China was revealed to some extent, along with the interaction between academics and politics in the past ten years, which is of great significance for the sustainable development and effective governance of China’s artificial intelligence.
Plastics Waste Metabolism in a Petro-Island State: Towards Solving a “Wicked Problem” in Trinidad and Tobago
Authors: Shah Kalim U. , Niles Keron , Ali Saleem H. , Surroop Dinesh , Jaggeshar Doorgeshwaree
Published: November 2019

Island systems have limited geographical, ecological, and social capacity to metabolize waste materials produced by the economic activities of their growing populations. Conceptualized as a ‘wicked problem’, the faults and weaknesses in waste management systems on islands continue to cause acute and cumulative ecological and human health impacts. Trinidad and Tobago is one such island jurisdiction grappling with this situation, particularly being a petroleum-dependent economy. Through the lens of neo-institutional theory, this case study of waste management in Trinidad and Tobago unpacks the efforts, reactions, drivers and circumstances that have led to various successes and failures but no definitive solutions over time, especially regarding plastics and packaging materials. We identify three temporal phases of policy evolution that have altered the waste metabolism trajectory to date: (1) government led patriarchal approach of traditional landfilling combined with behavioral change campaigns to reduce, reuse, and recycle, (2) to a more democratic, shared burden, public-private partnership approach combined with attempts at incentive-based regulations, (3) to the present, more private sector-led voluntary bans on production and use of plastics. This study contributes to our understanding of the institutional factors that shape the search for solutions to the wicked problem of island waste metabolism.