bluuebunny/crossref_metadata_2025 huggingface dataset:
Avocado Intake, and Longitudinal Weight and Body Mass Index Changes in an Adult Cohort
Authors: Heskey Celine , Oda Keiji , Sabaté Joan
Published: March 2019

Avocados contain nutrients and bioactive compounds that may help reduce the risk of becoming overweight/obese. We prospectively examined the effect of habitual avocado intake on changes in weight and body mass index (BMI). In the Adventist Health Study (AHS-2), a longitudinal cohort (~55,407; mean age ~56 years; U.S. and Canada), avocado intake (standard serving size 32 g/day) was assessed by a food frequency questionnaire (FFQ). Self-reported height and weight were collected at baseline. Self-reported follow-up weight was collected with follow-up questionnaires between four and 11 years after baseline. Using the generalized least squares (GLS) approach, we analyzed repeated measures of weight in relation to avocado intake. Marginal logistic regression analyses were used to calculate the odds of becoming overweight/obese, comparing low (>0 to <32 g/day) and high (≥32 g/day) avocado intake to non-consumers (reference). Avocado consumers who were normal weight at baseline, gained significantly less weight than non-consumers. The odds (OR (95% CI)) of becoming overweight/obese between baseline and follow-up was 0.93 (0.85, 1.01), and 0.85 (0.60, 1.19) for low and high avocado consumers, respectively. Habitual consumption of avocados may reduce adult weight gain, but odds of overweight/obesity are attenuated by differences in initial BMI values.
Bacterial sialoglycosidases in Virulence and Pathogenesis
Authors: Sudhakara Preethi , Sellamuthu Iyappan , Aruni A.
Published: March 2019

Human oral microbiome and dysbiotic infections have been recently evidently identified. One of the major reasons for such dysbiosis is impairment of the immune system. Periodontitis is a chronic inflammatory disease affecting the tissues that surround and support the teeth. In the United States., approximately 65 million people are affected by this condition. Its occurrence is also associated with many important systemic diseases such as cardiovascular disease, rheumatoid arthritis, and Alzheimer’s disease. Among the most important etiologies of periodontitis is Porphyromonas gingivalis, a keystone bacterial pathogen. Keystone pathogens can orchestrate inflammatory disease by remodeling a normally benign microbiota causing imbalance between normal and pathogenic microbiota (dysbiosis). The important characteristics of P. gingivalis causing dysbiosis are its virulence factors which cause effective subversion of host defenses to its advantage allowing other pathogens to grow. Some of the mechanisms involved in these processes are still not well-understood. However, various microbial strategies target host sialoglycoproteins for immune dysregulation. In addition, the enzymes that break down sialoglycoproteins and sialoglycans are the “sialoglycoproteases”, resulting in exposed terminal sialic acid. This process could lead to pathogen-toll like receptor (TLR) interactions mediated through sialic acid receptor ligand mechanisms. Assessing the function of P. gingivalis sialoglycoproteases, could pave the way to designing carbohydrate analogues and sialic acid mimetics to serve as drug targets.
Hyaluronic Acid-Modified and TPCA-1-Loaded Gold Nanocages Alleviate Inflammation
Authors: Zhao Jingnan
Published: March 2019

Gold nanocages (AuNCs) are biocompatible and porous nanogold particles that have been widely used in biomedical fields. In this study, hyaluronic acid (HA) and peptide- modified gold nanocages (HA-AuNCs/T/P) loaded with 2-[(aminocarbonyl)amino]-5-(4-fluorophenyl)-3-thiophenecarboxamide (TPCA-1) were prepared to investigate their potential for combating inflammation. TPCA-1 was released from AuNCs, intracellularly when HA was hydrolyzed by hyaluronidase. HA-AuNCs/T/P show a much higher intracellular uptake than AuNCs/T/P, and exhibit a much higher efficacy on the suppression of tumor necrosis factor alpha (TNF-α) and interleukin 6 (IL-6) than free TPCA-1, suggesting great improvement to the anti-inflammatory efficacy of TPCA-1 through the application of AuNCs. HA-AuNCs/T/P can also reduce the production of reactive oxygen species in inflammatory cells. This study suggests that HA-AuNCs/T/P may be potential agents for anti-inflammatory treatment, and are worthy of further investigation.
Dynamic Changes of Ascorbic Acid, Phenolics Biosynthesis and Antioxidant Activities in Mung Beans (Vigna radiata) until Maturation
Authors: Lu Yanyan , Chang Xiaoxiao , Guo Xinbo
Published: March 2019

To better understand the regulatory mechanism of phenolics and ascorbic acid accumulation as well as antioxidant activities in mung beans during legume development, the gene expression profiles of 25 key-coding genes in ascorbic acid and phenolics metabolic pathways were analyzed. As well as the dynamitic changes of ascorbic acid, phenolic profiles and antioxidant activities with legume development were studied. The results indicated that gene expression profiles were closely related to the ascorbic acid and phenolics accumulation regularity during legume development. VrVTC2 and VrGME played important roles for ascorbic acid accumulation from 8 to 17 days after flowering (DAF). VrPAL and VrCHS exhibited positive correlations with daidzein and glycitin accumulation, and VrIFS had a strong positive correlation with glycitin biosynthesis. Antioxidant activities dramatically increased during mung bean maturing, which were significantly related to ascorbic acid and phenolics accumulation. Eight days after flowering was the essential stage for ascorbic acid and phenolics biosynthesis in mung beans.
Electrical and Thermal Conductivity of Polylactic Acid (PLA)-Based Biocomposites by Incorporation of Nano-Graphite Fabricated with Fused Deposition Modeling
Authors: Guo Rui , Ren Zechun , Bi Hongjie , Xu Min , Cai Liping
Published: March 2019

The aim of the study was to improve the electrical and thermal conductivity of the polylactic acid/wood flour/thermoplastic polyurethane composites by Fused Deposition Modeling (FDM). The results showed that, when the addition amount of nano-graphite reached 25 pbw, the volume resistivity of the composites decreased to 108 Ω·m, which was a significant reduction, indicating that the conductive network was already formed. It also had good thermal conductivity, mechanical properties, and thermal stability. The adding of the redox graphene (rGO) combined with graphite into the composites, compared to the tannic acid-functionalized graphite or the multi-walled carbon nanotubes, can be an effective method to improve the performance of the biocomposites, because the resistivity reduced by one order magnitude and the thermal conductivity increased by 25.71%. Models printed by FDM illustrated that the composite filaments have a certain flexibility and can be printed onto paper or flexible baseplates.
Polymeric Antimicrobial Food Packaging and Its Applications
Authors: Huang Tianqi , Qian Yusheng , Wei Jia , Zhou Chuncai
Published: March 2019

Food corruption and spoilage caused by food-borne pathogens and microorganisms is a serious problem. As a result, the demand for antibacterial drugs in food packaging is growing. In this review, biodegradable and non-biodegradable materials for food packaging are discussed based on their properties. Most importantly, antibacterial agents are essential to inhibit the growth of bacteria in food. To keep food fresh and prolong the shelf life, different kinds of antibacterial agents were used. The composition and application of natural antibacterial agents and synthetic antibacterial agents are discussed. Compared with natural antibacterial agents, synthetic antibacterial agents have the advantages of low cost and high activity, but their toxicity is usually higher than that of natural antibacterial agents. Finally, future development of antimicrobial food packaging is proposed. It is an urgent problem for researchers to design and synthesize antibacterial drugs with high efficiency and low toxicity.
JointNet: A Common Neural Network for Road and Building Extraction
Authors: Zhang Zhengxin , Wang Yunhong
Published: March 2019

Automatic extraction of ground objects is fundamental for many applications of remote sensing. It is valuable to extract different kinds of ground objects effectively by using a general method. We propose such a method, JointNet, which is a novel neural network to meet extraction requirements for both roads and buildings. The proposed method makes three contributions to road and building extraction: (1) in addition to the accurate extraction of small objects, it can extract large objects with a wide receptive field. By switching the loss function, the network can effectively extract multi-type ground objects, from road centerlines to large-scale buildings. (2) This network module combines the dense connectivity with the atrous convolution layers, maintaining the efficiency of the dense connection connectivity pattern and reaching a large receptive field. (3) The proposed method utilizes the focal loss function to improve road extraction. The proposed method is designed to be effective on both road and building extraction tasks. Experimental results on three datasets verified the effectiveness of JointNet in information extraction of road and building objects.
Comparing Nadir and Multi-Angle View Sensor Technologies for Measuring in-Field Plant Height of Upland Cotton
Authors: Thompson Alison L. , Thorp Kelly R. , Conley Matthew M. , Elshikha Diaa M. , French Andrew N. , Andrade-Sanchez Pedro , Pauli Duke
Published: March 2019

Plant height is a morphological characteristic of plant growth that is a useful indicator of plant stress resulting from water and nutrient deficit. While height is a relatively simple trait, it can be difficult to measure accurately, especially in crops with complex canopy architectures like cotton. This paper describes the deployment of four nadir view ultrasonic transducers (UTs), two light detection and ranging (LiDAR) systems, and an unmanned aerial system (UAS) with a digital color camera to characterize plant height in an upland cotton breeding trial. The comparison of the UTs with manual measurements demonstrated that the Honeywell and Pepperl+Fuchs sensors provided more precise estimates of plant height than the MaxSonar and db3 Pulsar sensors. Performance of the multi-angle view LiDAR and UAS technologies demonstrated that the UAS derived 3-D point clouds had stronger correlations (0.980) with the UTs than the proximal LiDAR sensors. As manual measurements require increased time and labor in large breeding trials and are prone to human error reducing repeatability, UT and UAS technologies are an efficient and effective means of characterizing cotton plant height.
A New Global Total Electron Content Empirical Model
Authors: Feng Jiandi , Han Baomin , Zhao Zhenzhen , Wang Zhengtao
Published: March 2019

Research on total electron content (TEC) empirical models is one of the important topics in the field of space weather services. Global TEC empirical models based on Global Ionospheric Maps (GIMs) TEC data released by the International GNSS Service (IGS) have developed rapidly in recent years. However, the accuracy of such global empirical models has a crucial restriction arising from the non-uniform accuracy of IGS TEC data in the global scope. Specifically, IGS TEC data accuracy is higher on land and lower over the ocean due to the lack of stations in the latter. Using uneven precision GIMs TEC data as a whole for model fitting is unreasonable. Aiming at the limitation of global ionospheric TEC modelling, this paper proposes a new global ionospheric TEC empirical model named the TECM-GRID model. The model consists of 5183 sections, corresponding to 5183 grid points (longitude 5°, latitude 2.5°) of GIM. Two kinds of single point empirical TEC models, SSM-T1 and SSM-T2, are used for TECM-GRID. According to the locations of grid points, the SSM-T2 model is selected as the sub-model in the Mid-Latitude Summer Night Anomaly (MSNA) region, and SSM-T1 is selected as the sub-model in other regions. The fitting ability of the TECM-GRID model for modelling data was tested in accordance with root mean square (RMS) and relative RMS values. Then, the TECM-GRID model was validated and compared with the NTCM-GL model and Center for Orbit Determination in Europe (CODE) GIMs at time points other than modelling time. Results show that TECM-GRID can effectively describe the Equatorial Ionization Anomaly (EIA) and the MSNA phenomena of the ionosphere, which puts it in good agreement with CODE GIMs and means that it has better prediction ability than the NTCM-GL model.
Radiometric Cross-Calibration of GF-1 PMS Sensor with a New BRDF Model
Authors: Liu Qiyue , Yu Tao , Gao Hailiang
Published: March 2019

On-orbit radiometric calibration of a space-borne sensor is of great importance for quantitative remote sensing applications. Cross-calibration is a common method with high calibration accuracy, and the core and emphasis of this method is to select the appropriate reference satellite sensor. As for the cross-calibration of high-spatial resolution and narrow-swath sensor, however, there are some scientific issues, such as large observation angles of reference image, and non-synchronization (or quasi-synchronization) between the imaging date of reference image and the date of sensor to be calibrated, which affects the accuracy of cross-calibration to a certain degree. Therefore, taking the GaoFen-1 (GF-1) Panchromatic and Multi-Spectral (PMS) sensor as an example in this research, an innovative radiometric cross-calibration method is proposed to overcome this bottleneck. Firstly, according a set of criteria, valid MODIS (Moderate Resolution Imagine Spectroradiometer) images of sunny day in one year over the Dunhuang radiometric calibration site in China are extracted, and a new and distinctive bidirectional reflectance distribution function (BRDF) model based on top-of-atmosphere (TOA) reflectance and imaging angles of the sunny day MODIS images is constructed. Subsequently, the cross-calibration of PMS sensor at Dunhuang and Golmud radiation calibration test sites is carried out by using the method presented in this paper, taking the MODIS image with large solar and observation angles and Landsat 8 Operational Land Imager (OLI) with different dates from PMS as reference. The validation results of the calibration coefficients indicate that our proposed method can acquire high calibration accuracy, and the total calibration uncertainties of PMS using MODIS as reference sensor are less than 6%.
Enhanced Back-Projection as Postprocessing for Pansharpening
Authors: Liu Junmin , Ma Jing , Fei Rongrong , Li Huirong , Zhang Jiangshe
Published: March 2019

Pansharpening is the process of integrating a high spatial resolution panchromatic image with a low spatial resolution multispectral image to obtain a multispectral image with high spatial and spectral resolution. Over the last decade, several algorithms have been developed for pansharpening. In this paper, a technique, called enhanced back-projection (EBP), is introduced and applied as postprocessing on the pansharpening. The proposed EBP first enhances the spatial details of the pansharpening results by histogram matching and high-pass modulation, followed by a back-projection process, which takes into account the modulation transfer function (MTF) of the satellite sensor such that the pansharpening results obey the consistency property. The EBP is validated on four datasets acquired by different satellites and several commonly used pansharpening methods. The pansharpening results achieve substantial improvements by this postprocessing technique, which is widely applicable and requires no modification of existing pansharpening methods.
A Novel Underdetermined Blind Source Separation Method and Its Application to Source Contribution Quantitative Estimation
Authors: Lu Jiantao , Cheng Wei , Zi Yanyang
Published: March 2019

To identify the major vibration and radiation noise, a source contribution quantitative estimation method is proposed based on underdetermined blind source separation. First, the single source points (SSPs) are identified by directly searching the identical normalized time-frequency vectors of mixed signals, which can improve the efficiency and accuracy in identifying SSPs. Then, the mixing matrix is obtained by hierarchical clustering, and source signals can also be recovered by the least square method. Second, the optimal combination coefficients between source signals and mixed signals can be calculated based on minimum redundant error energy. Therefore, mixed signals can be optimally linearly combined by source signals via the coefficients. Third, the energy elimination method is used to quantitatively estimate source contributions. Finally, the effectiveness of the proposed method is verified via numerical case studies and experiments with a cylindrical structure, and the results show that source signals can be effectively recovered, and source contributions can be quantitatively estimated by the proposed method.
The Life of a New York City Noise Sensor Network
Authors: Mydlarz Charlie , Sharma Mohit , Lockerman Yitzchak , Steers Ben , Silva Claudio , Bello Juan Pablo
Published: March 2019

Noise pollution is one of the topmost quality of life issues for urban residents in the United States. Continued exposure to high levels of noise has proven effects on health, including acute effects such as sleep disruption, and long-term effects such as hypertension, heart disease, and hearing loss. To investigate and ultimately aid in the mitigation of urban noise, a network of 55 sensor nodes has been deployed across New York City for over two years, collecting sound pressure level (SPL) and audio data. This network has cumulatively amassed over 75 years of calibrated, high-resolution SPL measurements and 35 years of audio data. In addition, high frequency telemetry data have been collected that provides an indication of a sensors’ health. These telemetry data were analyzed over an 18-month period across 31 of the sensors. It has been used to develop a prototype model for pre-failure detection which has the ability to identify sensors in a prefail state 69.1% of the time. The entire network infrastructure is outlined, including the operation of the sensors, followed by an analysis of its data yield and the development of the fault detection approach and the future system integration plans for this.
A Water Quality Prediction Method Based on the Deep LSTM Network Considering Correlation in Smart Mariculture
Authors: Hu Zhuhua , Zhang Yiran , Zhao Yaochi , Xie Mingshan , Zhong Jiezhuo , Tu Zhigang , Liu Juntao
Published: March 2019

An accurate prediction of cage-cultured water quality is a hot topic in smart mariculture. Since the mariculturing environment is always open to its surroundings, the changes in water quality parameters are normally nonlinear, dynamic, changeable, and complex. However, traditional forecasting methods have lots of problems, such as low accuracy, poor generalization, and high time complexity. In order to solve these shortcomings, a novel water quality prediction method based on the deep LSTM (long short-term memory) learning network is proposed to predict pH and water temperature. Firstly, linear interpolation, smoothing, and moving average filtering techniques are used to repair, correct, and de-noise water quality data, respectively. Secondly, Pearson’s correlation coefficient is used to obtain the correlation priors between pH, water temperature, and other water quality parameters. Finally, a water quality prediction model based on LSTM is constructed using the preprocessed data and its correlation information. Experimental results show that, in the short-term prediction, the prediction accuracy of pH and water temperature can reach 98.56% and 98.97%, and the time cost of the predictions is 0.273 s and 0.257 s, respectively. In the long-term prediction, the prediction accuracy of pH and water temperature can reach 95.76% and 96.88%, respectively.
R-DEHM: CSI-Based Robust Duration Estimation of Human Motion with WiFi
Authors: Zhao Jijun , Liu Lishuang , Wei Zhongcheng , Zhang Chunhua , Wang Wei , Fan Yongjian
Published: March 2019

As wireless sensing has developed, wireless behavior recognition has become a promising research area, in which human motion duration is one of the basic and significant parameters to measure human behavior. At present, however, there is no consideration of the duration estimation of human motion leveraging wireless signals. In this paper, we propose a novel system for robust duration estimation of human motion (R-DEHM) with WiFi in the area of interest. To achieve this, we first collect channel statement information (CSI) measurements on commodity WiFi devices and extract robust features from the CSI amplitude. Then, the back propagation neural network (BPNN) algorithm is introduced for detection by seeking a cutting line of the features for different states, i.e., moving human presence and absence. Instead of directly estimating the duration of human motion, we transform the complex and continuous duration estimation problem into a simple and discrete human motion detection by segmenting the CSI sequences. Furthermore, R-DEHM is implemented and evaluated in detail. The results of our experiments show that R-DEHM achieves the human motion detection and duration estimation with the average detection rate for human motion more than 94% and the average error rate for duration estimation less than 8%, respectively.
Application of a Multi-Satellite Dynamic Mission Scheduling Model Based on Mission Priority in Emergency Response
Authors: Cui Jintian , Zhang Xin
Published: March 2019

Emergency observations are missions executed by Earth observation satellites to support urgent ground operations. Emergency observations become more important for meeting the requirements of highly dynamic and highly time-sensitive observation missions, such as disaster monitoring and early warning. Considering the complex scheduling problem of Earth observation satellites under emergency conditions, a multi-satellite dynamic mission scheduling model based on mission priority is proposed in this paper. A calculation model of mission priority is designed for emergency missions based on seven impact factors. In the satellite mission scheduling, the resource constraints of scheduling are analyzed in detail, and the optimization objective function is built to maximize the observation mission priority and mission revenues, and minimize the waiting time for missions that require urgency for execution time. Then, the hybrid genetic tabu search algorithm is used to obtain the initial satellite scheduling plan. In case of the dynamic arrival of new emergency missions before scheduling plan releases, a dynamic scheduling algorithm based on mission priority is proposed to solve the scheduling problem caused by newly arrived missions and to obtain the scheduling plan of newly arrived missions. A simulation experiment was conducted for different numbers of initial missions and newly arrived missions, and the scheduling results were evaluated with a model performance evaluation function. The results show that the execution probability of high-priority missions increased because the mission priority was taken into account in the model. In the case of more satellite resources, when new missions dynamically arrived, the satellite resources can be reasonably allocated to these missions based on the mission priority. Overall, this approach reduces the complexity of the dynamic adjustment and maintains the stability of the initial scheduling plan.
One-Stage Multi-Sensor Data Fusion Convolutional Neural Network for 3D Object Detection
Authors: Li Minle , Hu Yihua , Zhao Nanxiang , Qian Qishu
Published: March 2019

Three-dimensional (3D) object detection has important applications in robotics, automatic loading, automatic driving and other scenarios. With the improvement of devices, people can collect multi-sensor/multimodal data from a variety of sensors such as Lidar and cameras. In order to make full use of various information advantages and improve the performance of object detection, we proposed a Complex-Retina network, a convolution neural network for 3D object detection based on multi-sensor data fusion. Firstly, a unified architecture with two feature extraction networks was designed, and the feature extraction of point clouds and images from different sensors realized synchronously. Then, we set a series of 3D anchors and projected them to the feature maps, which were cropped into 2D anchors with the same size and fused together. Finally, the object classification and 3D bounding box regression were carried out on the multipath of fully connected layers. The proposed network is a one-stage convolution neural network, which achieves the balance between the accuracy and speed of object detection. The experiments on KITTI datasets show that the proposed network is superior to the contrast algorithms in average precision (AP) and time consumption, which shows the effectiveness of the proposed network.
LSPR Biosensing Approach for the Detection of Microtubule Nucleation
Authors: Hasegawa Keisuke , Nazarov Otabek , Porter Evan
Published: March 2019

Microtubules are dynamic protein filaments that are involved in a number of cellular processes. Here, we report the development of a novel localized surface plasmon resonance (LSPR) biosensing approach for investigating one aspect of microtubule dynamics that is not well understood, namely, nucleation. Using a modified Mie theory with radially variable refractive index, we construct a theoretical model to describe the optical response of gold nanoparticles when microtubules form around them. The model predicts that the extinction maximum wavelength is sensitive to a change in the local refractive index induced by microtubule nucleation within a few tens of nanometers from the nanoparticle surface, but insensitive to a change in the refractive index outside this region caused by microtubule elongation. As a proof of concept to demonstrate that LSPR can be used for detecting microtubule nucleation experimentally, we induce spontaneous microtubule formation around gold nanoparticles by immobilizing tubulin subunits on the nanoparticles. We find that, consistent with the theoretical model, there is a redshift in the extinction maximum wavelength upon the formation of short microtubules around the nanoparticles, but no significant change in maximum wavelength when the microtubules are elongated. We also perform kinetic experiments and demonstrate that the maximum wavelength is sensitive to the microtubule nuclei assembly even when microtubules are too small to be detected from an optical density measurement.
Proactive Deployment of Aerial Drones for Coverage over Very Uneven Terrains: A Version of the 3D Art Gallery Problem
Authors: Savkin Andrey V. , Huang Hailong
Published: March 2019

The paper focuses on surveillance and monitoring using aerial drones. The aim is to estimate the minimal number of drones necessary to monitor a given area of a very uneven terrain. The proposed problem may be viewed as a drone version of the 3D Art Gallery Problem. A computationally simple algorithm to calculate an upper estimate of the minimal number of drones together with their locations is developed. Computer simulations are conducted to demonstrate the effectiveness of the proposed method.
Spatial–Temporal Sensing and Utilization in Full Duplex Spectrum-Heterogeneous Cognitive Radio Networks for the Internet of Things
Authors: Khalid Waqas , Yu Heejung
Published: March 2019

The continuous growth of interconnected devices in the Internet of Things (IoT) presents a challenge in terms of network resources. Cognitive radio (CR) is a promising technology that can address the IoT spectral demands by enabling an opportunistic spectrum access (OSA) scheme. The application of full duplex (FD) radios in spectrum sensing enables secondary users (SUs) to perform sensing and transmission simultaneously, and improves the utilization of the spectrum. However, random and dense distributions of FD-enabled SU transmitters (FD-SU TXs) with sensing capabilities in small-cell CR-IoT environments poses new challenges, and creates heterogeneous environments with different spectral opportunities. In this paper, we propose a spatial and temporal spectral-hole sensing framework for FD-SU TXs deployed in CR-IoT spectrum-heterogeneous environment. Incorporating the proposed sensing model, we present the analytical formulation and an evaluation of a utilization of spectrum (UoS) scheme for FD-SU TXs present at different spatial positions. The numerical results are evaluated under different network and sensing parameters to examine the sensitivities of different parameters. It is demonstrated that self-interference, primary user activity level, and the sensing outcomes in spatial and temporal domains have a significant influence on the utilization performance of spectrum.
Using Machine Learning to Provide Reliable Differentiated Services for IoT in SDN-Like Publish/Subscribe Middleware
Authors: Shi Yulong , Zhang Yang , Jacobsen Hans-Arno , Tang Lulu , Elliott Geoffrey , Zhang Guanqun , Chen Xiwei , Chen Junliang
Published: March 2019

At present, most publish/subscribe middlewares suppose that there are equal Quality of Service (QoS) requirements for all users. However, in many real-world Internet of Things (IoT) service scenarios, different users may have different delay requirements. How to provide reliable differentiated services has become an urgent problem. The rise of Software-Defined Networking (SDN) provides endless possibilities to improve the QoS of publish/subscribe middlewares due to its greater programmability. We can encode event topics and priorities into flow entries of SDN switches directly to meet customized requirements. In this paper, we first propose an SDN-like publish/subscribe middleware architecture and describe how to use this architecture and priority queues supported by OpenFlow switches to realize differentiated services. Then we present a machine learning method using the eXtreme Gradient Boosting (XGBoost) model to solve the difficult issue of getting the queuing delay of switches accurately. Finally, we propose a reliable differentiated services guarantee mechanism according to the queuing delay and the programmability of SDN to improve QoS, namely, a two-layer queue management mechanism. Experimental evaluations show that the delay predicted by the XGBoost method is closer to the real value; our mechanism can save end-to-end delay, reduce packet loss rate, and allocate bandwidth more reasonably.
A Passive Source Location Method in a Shallow Water Waveguide with a Single Sensor Based on Bayesian Theory
Authors: Li Xiaoman , Piao Shengchun , Zhang Minghui , Liu Yan
Published: March 2019

Bayesian methodology is a good way to infer unknown parameters in a marine environment. A passive source location method in a shallow water waveguide with a single sensor based on Bayesian theory is presented in this paper. The input of a Bayesian inversion algorithm is received different normal mode impulse signals, which are separated and extracted with a warping transformation from received broadband impulse signals. The source range, depth, and other seabed parameters were estimated without prior knowledge of the seabed information. Different normal mode impulse acoustic signals travelling at different group speeds arrived at the sensor at different times because of the dispersion characteristics of the shallow water waveguide. The time delay of different modes can be used for the passive source location. However, normal mode group speeds are greatly affected by the environmental parameters. The performance of the passive location becomes negative when parameters mismatch. In this paper, the source location was transformed to the inversion of the source location and environmental parameters, which can be estimated accurately based on the multi-dimensional posterior probability density (PPD). This method is less limited by environmental factors, and the accuracy of inversion results can be analyzed according to the PPD of inversion parameters, which has higher reliability and a wider application scope. The effectiveness and robustness of the algorithm were quantified in terms of the root mean squared error (RMSE) at a variety of signal-to-noise ratios (SNRs) in 50 simulation sets. The RMSE values decreased with the SNR. The validity and accuracy of the method were proved by the results of simulation and experiment data.
FRET-Based Semiconducting Polymer Dots for pH Sensing
Authors: Ou Jiemei , Tan Huijun , Chen Zhong , Chen Xudong
Published: March 2019

Förster resonance energy transfer (FRET)-based polymer dots (Pdots), fabricated by semiconducting polymers and exhibiting excellent properties, have attracted much interest in the last decade, however, full polymer-dot-based pH sensors are seldom systematically exploited by researchers. In this work, we constructed a kind of blend polymer dot, utilizing poly[(9,9-dihexyl-9H-fluorene-2,7-vinylene)-co-(1-methoxy-4-(2-ethylhexyloxy)-2,5-phenylenevinylene)] (PFV) as the donor, poly[2,5-bis(3′,7′-dimethyloctyloxy)-1,4-phenylenevinylene] (BDMO-PPV) as the acceptor, and polysytrene graft EO functionalized with carboxy (PS-PEG-COOH) to generate surface carboxyl groups. This type of Pdot, based on the FRET process, was quite sensitive to pH value changes, especially low pH environments. When the pH value decreases down to 2 or 1, the fluorescence spectrum of Pdots-20% exhibit spectral and intensity changes at the same time, and fluorescence lifetime changes as well, which enables pH sensing applications. The sharpening of the emission peak at ~524 nm, along with the weakening and blue shifts of the emission band at ~573 nm, imply that the efficiency of the energy transfer between PFV and BDMO-PPV inside the Pdots-20% decreased due to polymer chain conformational changes. The time-resolved fluorescence measurements supported this suggestion. Pdots constructed by this strategy have great potential in many applications, such as industrial wastewater detection, in vitro and intracellular pH measurement, and DNA amplification and detection.
Governance and Degrowth. Lessons from the 2008 Financial Crisis in Latvia and Iceland
Authors: Nyblom Åsa , Isaksson Karolina , Sanctuary Mark , Fransolet Aurore , Stigson Peter
Published: March 2019

This paper investigates the role of governance dimensions in socio-economic transitions in line with degrowth, i.e., an equitable downscaling of the economy. Our focus is on experiences from the 2008 economic crisis in Latvia and Iceland. Although these cases are not in themselves examples of degrowth, we see them as important sources of empirical learning from major socio-economical transitions; furthermore, we see crises as possible starting points for future degrowth transitions. This paper applies a governance framework to explore the vast differences in management strategies and crisis outcomes in Latvia and Iceland. In Iceland, public resistance led to a shift in policy measures such that economic inequality and the negative social consequences of the crisis decreased. In Latvia, public resistance existed but had no strong influence. The outcome in Latvia included none of the elements of equitable downscaling found in the case of Iceland. These two cases show how differences in formal institutional arrangements, political culture and societal trust affect different governance dimensions during a time of crisis. The analysis illustrates the importance of institutional and governance dimensions in major socio-economical transitions, and demonstrates how they influence the kind of transition that can be realized.
Daily Accessed Street Greenery and Housing Price: Measuring Economic Performance of Human-Scale Streetscapes via New Urban Data
Authors: Ye Yu , Xie Hanting , Fang Jia , Jiang Hetao , Wang De
Published: March 2019

The protective effects of street greenery on ecological, psychological, and behavioral phenomena have been well recognized. Nevertheless, the potential economic effect of daily accessed street greenery, i.e., a human-scale and perceptual-oriented quality focusing on exposure to street greenery in people’s daily lives, has not been fully studied because a quantitative measuring of this human-scale indicator is hard to achieve. This study was an attempt in this direction with the help of new urban data and new analytical tools. Shanghai, which has a mature real estate market, was selected for study, and the housing prices of 1395 private neighborhoods in its city center were collected. We selected more than forty variables that were classified under five categories—location features, distances to the closest facilities, density of facilities within a certain radius, housing and neighborhood features, and daily accessed street greenery—in a hedonic pricing model. The distance and density of facilities were computed through a massive number of points-of-interest and a geographical information system. The visible street greenery was collected from Baidu street view images and then measured via a machine-learning algorithm, while accessibility was measured through space syntax. In addition to the well-recognized effects previously discovered, the results show that visible street greenery and street accessibility at global scale hold significant positive coefficients for housing prices. Visible street greenery even obtains the second-highest regression coefficient in the model. Moreover, the combined assessment, the co-presence of local-scale accessibility and eye-level greenery, is significant for housing price as well. This study provides a scientific and quantitative support for the significance of human-scale street greenery, making it an important issue in urban greening policy for urban planners and decision makers.
Spatial Correlation, Influencing Factors and Environmental Supervision on Mechanism Construction of Atmospheric Pollution: An Empirical Study on SO2 Emissions in China
Authors: Yang Ruoyu , Chen Weidong
Published: March 2019

In order to study the present situation regarding SO2 emissions in China, problems are identified and countermeasures and suggestions are put forward. This paper analyzes spatial correlation, influencing factors and regulatory tools of air pollution in 30 provinces on the Chinese mainland from 2006–2015. The results of exploratory spatial data analysis (ESDA) show that SO2 emissions have obvious positive spatial correlations, and atmospheric pollution in China shows obvious spatial overflow effects and spatial agglomeration characteristics. On this basis, the present study analyzes the impact of seven socioeconomical (SE) factors and seven policy tools on air pollution by constructing a STIRPAT model and a spatial econometric model. We found that population pressure, affluence, energy consumption (EC), industrial development level (ID), urbanization level (UL) and the degree of marketization can significantly promote the increase of SO2 emissions, but technology and governmental supervision of the environment have significant inhibitory effects. The reason why China’s air pollution is curbed at present is because the government has adopted a large number of powerful command-controlled supervision measures, to a large extent. Air pollution treatment is like a government-led “political movement”. The effect of the market is relatively weak and public force has not been effectively exerted. In the future, a comprehensive use of a variety of regulation tools is needed, as well as encouraging the public to participate, strengthening the supervision of third parties and building a diversified and all-encompassing supervision mechanism.
Productive Employment for Inclusive and Sustainable Development in European Union Countries: A Multivariate Analysis
Authors: Georgescu Maria-Ana , Herman Emilia
Published: March 2019

It is widely recognized that achieving highly productive employment is a serious challenge facing inclusive and sustainable development. In this context, the aim of this article was to highlight the main characteristics and mechanisms of productive employment, focusing on the interrelationships between productive employment, and inclusive and sustainable development in European Union countries, during the recent economic crisis and recovery period (2007–2016). The results of the correlation and regression analysis suggest that the high level of inclusive and sustainable development in some European Union countries can be mainly explained by high labor productivity, an efficient sectoral structure of employment, a low level of vulnerable and precarious employment, and low working poverty. Moreover, the results of the principal component analysis and cluster analysis show that there are common features and differences between the European Union member states in terms of their interrelationship between productive employment, and inclusive and sustainable development, which emphasizes the need to take specific actions to transform unproductive employment into productive employment, especially in southern countries and some central and eastern European countries, so that productive employment will be the driving force for development.
Learning or Relaxing: How Do Challenge Stressors Stimulate Employee Creativity?
Authors: Sun Yongbo , Hu Xiaojuan , Ding Yixin
Published: March 2019

As important situational factors in the workplace, challenge stressors play an important role in stimulating employee creativity. This study used self-efficacy and emotional exhaustion as intervening processes to delve into the impact of promotion and depletion mechanisms of challenge stressors on employee creativity. According to the theory of resource conservation, the study explores the moderating effect of learning and relaxing at work on the promotion and depletion mechanisms of challenge stressors. Fuzzy-set qualitative comparative analysis was conducted to analyze the effect of a combination of factors on employee creativity. A total of 240 valid paired-samples were collected from employees of three enterprises in information technology, finance, and evaluation services industries. This study drew the following conclusions. Challenge stressors have a direct positive effect on employee creativity, self-efficacy and emotional exhaustion have partial mediating effects on the relationship between challenge stressors and employee creativity, learning positively moderates the relationship between challenge stressors and self-efficacy, and qualitative comparative analysis reveals three configurations that improve employee creativity.
Spatial Analysis of Big Data Industrial Agglomeration and Development in China
Authors: Lu Yanru , Cao Kai
Published: March 2019

Nowadays, our daily life constantly creates and needs to utilize tremendous amounts of datasets. Fortunately, the technologies of the internet, both in software and hardware, have the capability to transmit, store, and operate big data. With China being the most populous country in the world, developing the big data industry is, therefore, seen as an urgent task. As generating industrial agglomeration is important for forming a mature industry, this study aims to characterize the phenomenon of big data industrial agglomeration in China, and to identify the factors for developing the big data industry using spatial analysis approaches and GIS technology from a geographer’s perspective. The problems and strengths of these representative cities are discussed, from which the solutions and the possible directions for the future are also provided. The findings argued that China is still at the primary stage of the development in the big data industry. Only several cities had the presence of a strong agglomeration, but the intercity space spillover was weak. However, comparing the changes in industry distribution, the trend of agglomeration have appeared, and the benefits of industrial agglomeration have also worked. The principal factors of the big data industry and its agglomeration include the support of government and the outstanding higher education agglomeration. In addition, it was also noted that each city has its own characteristics and potentials to attract more big data enterprises, talent, and investment.
Effects of Higher Education Levels on Total Factor Productivity Growth
Authors: Liu Jie , Bi Chao
Published: March 2019

China is facing challenges to sustainable economic growth. Higher education of Chinese residents can affect total factor productivity (TFP) growth and hence has an influence on economic sustainability. However, currently, there is limited literature on the nexus between higher education and TFP in China. Therefore, this paper empirically analyzes the heterogeneous and spatial effect of higher education on the regional TFP growth using a dynamic spatial econometric model with provincial panel data from 2003 to 2016. The results indicate that different levels of higher education have significant effects on TFP growth and are mainly reflected in the spatial spillover effect. Bachelor and doctoral education (particularly doctoral education) demonstrated significant positive effects, whereas the technical school and master education had significant negative effects. When decomposing this effect into technical efficiency and technical progress to explore the mechanism of influences, the latter plays the major role. Therefore, the Chinese government can promote TFP growth and economic sustainability by expanding the scale of bachelor and doctoral education and improving the quality of technical and master education.
Evaluation of the Competitiveness of China’s Commercial Banks Based on the G-CAMELS Evaluation System
Authors: Guan Fangyuan , Liu Chuanzhe , Xie Fangming , Chen Huiying
Published: March 2019

On the basis of the original camel rating system, this study added the green indicator and formed the G-CAMELS evaluation system (An improved rating system based on the CAMELS rating system to evaluate the business operation of financial institutions more comprehensively.) to comprehensively evaluate the competitiveness of commercial banks. It followed China’s current requirements for the sustainable development of commercial banks. In this paper, factor analysis, entropy methods, and dynamic evaluation models are used to obtain the ranking of competitiveness. In addition, according to the same steps as above, the comprehensive ranking based on the CAMELS evaluation system (A comprehensive rating system which is standardized, institutionalized and indexed for business operations of commercial banks and other financial institutions.) was obtained. The two ranking systems were compared. It is found that with the entropy weight method, in the G-CAMELS system, the weight of the green index is quite large, so it magnifies the impact of the financial industry on the environment. Compared with the original CAMELS system, the newly formed system will increase the ranking of state-owned banks and there is no significant change in the ranking of joint-stock banks. In order to improve the competitiveness of banks, state-owned banks should innovate their banking business and continue to implement the green credit policy; joint-stock banks should continue to seize the opportunity of green credit and expand profitability while paying attention to safety. In addition, the government could consider relaxing green credit standards for city commercial banks to ease pressure on banks.