bluuebunny/crossref_metadata_2025 huggingface dataset:
Ciprofloxacin-Modified Degradable Hybrid Polyurethane-Polylactide Porous Scaffolds Developed for Potential Use as an Antibacterial Scaffold for Regeneration of Skin
Authors: Iga Carayon , Agata Terebieniec , Marcin Łapiński , Natalia Filipowicz , Justyna Kucińska-Lipka
Published: January 2020

The aim of the performed study was to fabricate an antibacterial and degradable scaffold that may be used in the field of skin regeneration. To reach the degradation criterion for the biocompatible polyurethane (PUR), obtained by using amorphous α,ω-dihydroxy(ethylene-butylene adipate) macrodiol (PEBA), was used and processed with so-called “fast-degradable” polymer polylactide (PLA) (5 or 10 wt %). To meet the antibacterial requirement obtained, hybrid PUR-PLA scaffolds (HPPS) were modified with ciprofloxacin (Cipro) (2 or 5 wt %) and the fluoroquinolone antibiotic inhibiting growth of bacteria, such as Pseudomonas aeruginosa, Escherichia coli, and Staphylococcus aureus, which are the main causes of wound infections. Performed studies showed that Cipro-modified HPPS, obtained by using 5% of PLA, possess suitable mechanical characteristics, morphology, degradation rates, and demanded antimicrobial properties to be further developed as potential scaffolds for skin tissue engineering.
Surface Alkylation of Cellulose Nanocrystals to Enhance Their Compatibility with Polylactide
Authors: Lee Joo Hyung , Park Sang Ho , Kim Seong Hun
Published: January 2020

Effective surface alkylation of cellulose nanocrystals (CNCs) was developed using a nucleophilic substitution reaction with an alkyl bromide to convert hydrophilic groups on the CNCs into alkyl groups and the degree of substitution was quantitatively determined. The resultant alkylated CNCs exhibited improved dispersion in a nonpolar environment and increased hydrophobicity, compared with unmodified and acetylated CNCs. Polylactide (PLA) nanocomposites reinforced with unmodified and modified CNCs were prepared by a solution casting method and the effects of reinforcement on the thermal stability, mechanical properties, morphology, and barrier properties were investigated. In addition, modeling of the mechanical properties was evaluated to simulate the modulus of the PLA nanocomposites and results were compared with the experimental values. PLA nanocomposites reinforced with alkylated CNCs exhibited superior properties in terms of thermal stability, tensile strength, Young’s modulus, and barrier properties because of the uniform dispersion and strong interfacial adhesion between filler and matrix. This high performance and fully return-to-nature nanocomposite is expected to expand the utilization of CNCs from sustainable bioresources and the practical application of biodegradable plastics.
Bio-Inspired Amphiphilic Block-Copolymers Based on Synthetic Glycopolymer and Poly(Amino Acid) as Potential Drug Delivery Systems
Authors: Levit Mariia , Zashikhina Natalia , Vdovchenko Alena , Dobrodumov Anatoliy , Zakharova Natalya , Kashina Anna , Rühl Eckart , Lavrentieva Antonina , Scheper Thomas , Tennikova Tatiana , Korzhikova-Vlakh Evgenia
Published: January 2020

In this work, a method to prepare hybrid amphiphilic block copolymers consisting of biocompatible synthetic glycopolymer with non-degradable backbone and biodegradable poly(amino acid) (PAA) was developed. The glycopolymer, poly(2-deoxy-2-methacrylamido-D-glucose) (PMAG), was synthesized via reversible addition-fragmentation chain transfer (RAFT) polymerization. Two methods for modifying the terminal dithiobenzoate-group of PMAG was investigated to obtain the macroinitiator bearing a primary aliphatic amino group, which is required for ring-opening polymerization of N-carboxyanhydrides of hydrophobic α-amino acids. The synthesized amphiphilic block copolymers were carefully analyzed using a set of different physico-chemical methods to establish their composition and molecular weight. The developed amphiphilic copolymers tended to self-assemble in nanoparticles of different morphology that depended on the nature of the hydrophobic amino acid present in the copolymer. The hydrodynamic diameter, morphology, and cytotoxicity of polymer particles based on PMAG-b-PAA were evaluated using dynamic light scattering (DLS) and transmission electron microscopy (TEM), as well as CellTiter-Blue (CTB) assay, respectively. The redox-responsive properties of nanoparticles were evaluated in the presence of glutathione taken at different concentrations. Moreover, the encapsulation of paclitaxel into PMAG-b-PAA particles and their cytotoxicity on human lung carcinoma cells (A549) and human breast adenocarcinoma cells (MCF-7) were studied.
Validation of Nanoparticle Response to the Sound Pressure Effect during the Drug-Delivery Process
Authors: Abbas Mohamed , Alqahtani Mohammed , Algahtani Ali , Kessentini Amir , Loukil Hassen , Parayangat Muneer , Ijyas Thafasal , Mohammed Abdul Wase
Published: January 2020

Intravenous delivery is the fastest conventional method of delivering drugs to their targets in seconds, whereas intramuscular and subcutaneous injections provide a slower continuous delivery of drugs. In recent years, nanoparticle-based drug-delivery systems have gained considerable attention. During the progression of nanoparticles into the blood, the sound waves generated by the particles create acoustic pressure that affects the movement of nanoparticles. To overcome this issue, the impact of sound pressure levels on the development of nanoparticles was studied herein. In addition, a composite nanostructure was developed using different types of nanoscale substances to overcome the effect of sound pressure levels in the drug-delivery process. The results demonstrate the efficacy of the proposed nanostructure based on a group of different nanoparticles. This study suggests five materials, namely, polyimide, acrylic plastic, Aluminum 3003-H18, Magnesium AZ31B, and polysilicon for the design of the proposed structure. The best results were obtained in the case of the movement of these molecules at lower frequencies. The performance of acrylic plastic is better than other materials; the sound pressure levels reached minimum values at frequencies of 1, 10, 20, and 60 nHz. Furthermore, an experimental setup was designed to validate the proposed idea using advanced biomedical imaging technologies. The experimental results demonstrate the possibilities of detecting, tracking, and evaluating the movement behaviors of nanoparticles. The experimental results also demonstrate that the lowest sound pressure levels were observed at lower frequency levels, thus proving the validity of the proposed computational model assumptions. The outcome of this study will pave the way to understand the interaction behaviors of nanoparticles with the surrounding biological environments, including the sound pressure effect, which could lead to the useof such an effect in facilitating directional and tactic movements of the micro- and nano-motors.
Characterization of Lignin Structures in Phyllostachys edulis (Moso Bamboo) at Different Ages
Authors: Zhu Yikui , Huang Jiawei , Wang Kaili , Wang Bo , Sun Shaolong , Lin Xinchun , Song Lili , Wu Aimin , Li Huiling
Published: January 2020

Bamboo is a gramineous plant widely distributed in China and has great prospects. Normally, local people cut bamboo culm at first year for paper milling or at six years for construction. Understanding lignin changes in bamboo with aging is necessary for better exploring the application of bamboo at different ages and can also promote the application of bamboo more effectively. Based on the previous study, the chemical structure and the lignin content of bamboo at different ages were further explored by FT-IR, GPC, NMR and other chemical methods in this paper. Results showed that the lignin structures of bamboo at different ages were similar with three monomers of S, G and H, but the molecular weight increased with age. Quantitative structure estimation further confirmed that S-type lignin content and S/G ratio of bamboo lignin constantly increased with age.
High-Precision Monitoring of Average Molecular Weight of Polyethylene Wax from Waste High-Density Polyethylene
Authors: Guo Zhouchao , Lan Xia , Xue Ping
Published: January 2020

High-density polyethylene (HDPE) is a major component of polyethylene waste, yet only under 29.9% of waste HDPE is recycled. As an important additive, polyethylene wax (PEW) is increasingly used in many industries such as plastics, dyes, and paints. The preparation of PEW has received considerable interest because recycling and precisely controllable production can bring huge economic benefits. In this study, to recycle waste HDPE, a single screw extruder was innovatively combined with a connecting pipe to prepare PEW from the pyrolysis of waste HDPE. Using a test platform, PEWs were prepared under different pyrolysis temperatures and screw speeds, and corresponding number-average molecular weights (NAMWs) of PEWs were measured. To precisely monitor NAMW of PEW, a program was developed in MATLAB. First, the relationship between NAMW and pyrolysis ratio was obtained, and a measure-point-independence verification was conducted. Then, modified Arrhenius equations and time-dependent pyrolysis temperature were for the first time introduced into the HDPE pyrolysis model. Furthermore, the screw-speed-dependent inverse method was proposed and validated for high-precision monitoring of NAMW of PEW from the pyrolysis of waste HDPE by extrusion. PEW of desired molecular weight was able to be precisely obtained from waste HDPE.
A Geographically Weighted Regression Approach to Understanding Urbanization Impacts on Urban Warming and Cooling: A Case Study of Las Vegas
Authors: Wang Zhe , Fan Chao , Zhao Qunshan , Myint Soe Win
Published: January 2020

A surface urban heat island (SUHI) effect is one of the most significant consequences of urbanization. Great progress has been made in evaluating the SUHI with cross-sectional studies performed in a number of cities across the globe. Few studies; however, have focused on the spatiotemporal changes in an area over a long period of time. Using multi-temporal remote sensing data sets, this study examined the spatiotemporal changes of the SUHI intensity in Las Vegas, Nevada, over a 15-year period from 2001 to 2016. We applied the geographically weighted regression (GWR) and advanced statistical approaches to investigating the SUHI variation in relation to several important biophysical indicators in the region. The results show that (1) Las Vegas had experienced a significant increase in the SUHI over the 15 years, (2) Vegetation and large and small water bodies in the city can help mitigate the SUHI effect and the cooling effect of vegetation had increased continuously from 2001 to 2016, (3) An urban heat sink (UHS) was identified in developed areas with low to moderate intensity, and (4) Increased surface temperatures were mainly driven by the urbanization-induced land conversions occurred over the 15 years. Findings from this study will inspire thoughts on practical guidelines for SUHI mitigation in a fast-growing desert city.
Towards an Operational, Split Window-Derived Surface Temperature Product for the Thermal Infrared Sensors Onboard Landsat 8 and 9
Authors: Gerace Aaron , Kleynhans Tania , Eon Rehman , Montanaro Matthew
Published: January 2020

The split window technique has been used for over thirty years to derive surface temperatures of the Earth with image data collected from spaceborne sensors containing two thermal channels. The latest NASA/USGS Landsat satellites contain the Thermal Infrared Sensor (TIRS) instruments that acquire Earth data in two longwave infrared bands, as opposed to a single band with earlier Landsats. The United States Geological Survey (USGS) will soon begin releasing a surface temperature product for Landsats 4 through 8 based on the single spectral channel methodology. However, progress is being made toward developing and validating a more accurate and less computationally intensive surface temperature product based on the split window method for Landsat 8 and 9 datasets. This work presents the progress made towards developing an operational split window algorithm for TIRS. Specifically, details of how the generalized split window algorithm was tailored for the TIRS sensors are presented, along with geometric considerations that should be addressed to avoid spatial artifacts in the final surface temperature product. Validation studies indicate that the proposed algorithm is accurate to within 2 K when compared to land-based measurements and to within 1 K when compared to water-based measurements, highlighting the improved accuracy that may be achieved over the current single-channel methodology being used to derive surface temperature in the Landsat Collection 2 surface temperature product. Surface temperature products using the split window methodologies described here can be made available upon request for testing purposes.
Optimal Temporal Window Selection for Winter Wheat and Rapeseed Mapping with Sentinel-2 Images: A Case Study of Zhongxiang in China
Authors: Meng Shiyao , Zhong Yanfei , Luo Chang , Hu Xin , Wang Xinyu , Huang Shengxiang
Published: January 2020

Currently, the main remote sensing-based crop mapping methods are based on spectral-temporal features. However, there has been a lack research on the selection of the multi-temporal images, and most of the methods are based on the use of all the available images during the cycle of crop growth. In this study, in order to explore the optimal temporal window for crop mapping with limited remote sensing data, we tested all possible combinations of temporal windows in an exhaustive manner, and made a comprehensive consideration of the spatial accuracy and statistical accuracy as evaluation indices. We collected all the available cloud-free Sentinel-2 multi-spectral images for the winter wheat and rapeseed growth periods in the study area in southern China, and used the random forest (RF) method as the classifier to identify the optimal temporal window. The spatial and statistical accuracies of all the results were assessed by using ground survey data and local agricultural census data. The optimal temporal window for the mapping of winter wheat and rapeseed in the study area was obtained by identifying the best-performing set of results. In addition, the variable importance (VI) index was used to evaluate the importance of the different bands for crop mapping. The results of the spatial accuracy, statistical accuracy, and the VI showed that the combinations of images from the later stages of crop growth were more suitable for crop mapping.
Multi-Channel Ground-Penetrating Radar Array Surveys of the Iron Age and Medieval Ringfort Bårby on the Island of Öland, Sweden
Authors: Viberg Andreas , Gustafsson Christer , Andrén Anders
Published: January 2020

As a part of the project “The Big Five”, large-scale multi-channel ground-penetrating radar surveys were carried out at Bårby ringfort (Swedish: borg), Öland, Sweden. The surveys were carried out using a MALÅ Imaging Radar Array (MIRA) system and aimed at mapping possible buried Iron Age and Medieval remains through the interior in order to better understand the purpose of the fort during its periods of use. An additional goal was to evaluate the impact of earlier farming on the preservation of the archaeological remains. The data provided clear evidence of well-preserved Iron Age and Medieval buildings inside the fort. The size and the pattern of the Iron Age houses suggest close similarities with, for example, the previously excavated fort at Eketorp on Öland. Given the presence of a substantial cultural layer together with a large number of artefacts recovered during a metal detection survey, it is suggested that Bårby borg’s primary function during the Iron Age was as a fortified village. The Medieval houses partly cover some of the Iron Age buildings. They are placed in a U-shape with an open square in the middle facing the edge of a limestone cliff. As in the case of Eketorp, it is suggested that the activities during Medieval times changed, but the precise purpose of the Medieval Bårby settlement is still a question open for debate. Future targeted archaeological investigations are needed in order to better understand its purpose. Rescue excavations may also be necessary, as the western steep cliff ledge is eroding and the well-preserved archaeological remains are at risk of being destroyed.
Understanding Inter-Hemispheric Traveling Ionospheric Disturbances and Their Mechanisms
Authors: Jonah Olusegun F. , Zhang Shunrong , Coster Anthea J. , Goncharenko Larisa P. , Erickson Philip J. , Rideout William , de Paula Eurico R. , de Jesus Rodolfo
Published: January 2020

Traveling ionospheric disturbances (TIDs) are wave-like disturbances in ionospheric plasma density. They are often observed during both quiet (medium-scale TID) and geomagnetically disturbed (large-scale TID) conditions. Their amplitudes can reach double-digit percentages of the background plasma density, and their existence presents a challenge for accurate ionosphere specification. In this study, we examine TID properties using observations obtained during two geomagnetically disturbed periods using multiple ground and space-borne instruments, such as magnetometers, Global Navigation Satellite System (GNSS) receivers, and the SWARM satellite. Reference quiet time observations are also provided for both storms. We use a thermosphere–ionosphere–electrodynamics general circulation model (TIEGCM) results to properly interpret TID features and their drivers. This combination of observations and modeling allows the investigation of variations of TID generation mechanisms and subsequent wave propagation, particularly as a function of different plasma background densities during various geophysical conditions. The trans-equatorial coupling of TIDs in the northern and southern hemispheres is also investigated with respect to attenuation and propagation characteristics. We show that TID properties during trans-equatorial events may be substantially affected by storm time background neutral wind perturbation.
SAR Interferometric Baseline Refinement Based on Flat-Earth Phase without a Ground Control Point
Authors: Xu Bing , Li Zhiwei , Zhu Yan , Shi Jiancun , Feng Guangcai
Published: January 2020

Interferometric baseline estimation is a key procedure of interferometric synthetic aperture radar (SAR) data processing. The error of the interferometric baseline affects not only the removal of the flat-earth phase, but also the transformation coefficient between the topographic phase and elevation, which will affect the topographic phase removal for differential interferometric SAR (D-InSAR) and the accuracy of the final generated digital elevation model (DEM) product for interferometric synthetic aperture (InSAR). To obtain a highly accurate interferometric baseline, this paper firstly investigates the geometry of InSAR imaging and establishes a rigorous relationship between the interferometric baseline and the flat-earth phase. Then, a baseline refinement method without a ground control point (GCP) is proposed, where a relevant theoretical model and resolving method are developed. Synthetic and real SAR datasets are used in the experiments, and a comparison with the conventional least-square (LS) baseline refinement method is made. The results demonstrate that the proposed method exhibits an obvious improvement over the conventional LS method, with percentages of up to 51.5% in the cross-track direction. Therefore, the proposed method is effective and advantageous.
A Spatially Transferable Drought Hazard and Drought Risk Modeling Approach Based on Remote Sensing Data
Authors: Schwarz Maximilian , Landmann Tobias , Cornish Natalie , Wetzel Karl-Friedrich , Siebert Stefan , Franke Jonas
Published: January 2020

Drought adversely affects vegetation conditions and agricultural production and consequently the food security and livelihood situation of the often most vulnerable communities. In spite of recent advances in modeling drought risk and impact, coherent and explicit information on drought hazard, vulnerability and risk is still lacking over wider areas. In this study, a spatially explicit drought hazard, vulnerability, and risk modeling framework was investigated for agricultural land, grassland and shrubland areas. The developed drought hazard model operates on a higher spatial resolution than most available drought models while also being scalable to other regions. Initially, a logistic regression model was developed to predict drought hazard for rangelands and croplands in the USA. The drought hazard model was cross-verified for the USA using the United States Drought Monitor (USDM). The comparison of the model with the USDM showed a good spatiotemporal agreement, using visual interpretation. Subsequently, the explicit and accurate USA model was transferred and calibrated for South Africa and Zimbabwe, where drought vulnerability and drought risk were assessed in combination with drought hazard. The drought hazard model used time series crop yields data from the Food and Agriculture Organization Corporate Statistical Database (FAOSTAT) and biophysical predictors from satellite remote sensing (SPI, NDVI, NDII, LST, albedo). A McFadden’s Pseudo R² value of 0.17 for the South African model indicated a good model fit. The plausibility of the drought hazard model results in southern Africa was evaluated by using regional climate patterns, published drought reports and a visual comparison to a global drought risk model and food security classification data. Drought risk and vulnerability were assessed for southern Africa and could also be spatially explicit mapped showing, for example, lower drought vulnerability and risk over irrigated areas. The innovative aspect of the presented drought hazard model is that it can be applied to other countries at a global scale, since it only uses globally available data sets and therefore can be easily modified to account for country-specific characteristics. At the same time, it can capture regional drought conditions through a higher resolution than other existing global drought hazard models. This model addressed the gap between global drought models, that cannot spatially and temporally explicitly capture regional drought effects, and sub-regional drought models that may be spatially explicit but not spatially transferable. Since we used globally available and spatially consistent data sets (both as predictors and response variables), the approach of this study can potentially be used globally to enhance existing modelling routines, drought intervention strategies and preparedness measures.
Deep TEC: Deep Transfer Learning with Ensemble Classifier for Road Extraction from UAV Imagery
Authors: Senthilnath J. , Varia Neelanshi , Dokania Akanksha , Anand Gaotham , Benediktsson Jón Atli
Published: January 2020

Unmanned aerial vehicle (UAV) remote sensing has a wide area of applications and in this paper, we attempt to address one such problem—road extraction from UAV-captured RGB images. The key challenge here is to solve the road extraction problem using the UAV multiple remote sensing scene datasets that are acquired with different sensors over different locations. We aim to extract the knowledge from a dataset that is available in the literature and apply this extracted knowledge on our dataset. The paper focuses on a novel method which consists of deep TEC (deep transfer learning with ensemble classifier) for road extraction using UAV imagery. The proposed deep TEC performs road extraction on UAV imagery in two stages, namely, deep transfer learning and ensemble classifier. In the first stage, with the help of deep learning methods, namely, the conditional generative adversarial network, the cycle generative adversarial network and the fully convolutional network, the model is pre-trained on the benchmark UAV road extraction dataset that is available in the literature. With this extracted knowledge (based on the pre-trained model) the road regions are then extracted on our UAV acquired images. Finally, for the road classified images, ensemble classification is carried out. In particular, the deep TEC method has an average quality of 71%, which is 10% higher than the next best standard deep learning methods. Deep TEC also shows a higher level of performance measures such as completeness, correctness and F1 score measures. Therefore, the obtained results show that the deep TEC is efficient in extracting road networks in an urban region.
Sensitivity Analysis of the DART Model for Forest Mensuration with Airborne Laser Scanning
Authors: Roberts Osian , Bunting Pete , Hardy Andy , McInerney Daniel
Published: January 2020

Airborne Laser Scanning (ALS) measurements are increasingly vital in forest management and national forest inventories. Despite the growing reliance on ALS data, comparatively little research has examined the sensitivity of ALS measurements to varying survey conditions over commercially important forests. This study investigated: (i) how accurately the Discrete Anisotropic Radiative Transfer (DART) model was able to replicate small-footprint ALS measurements collected over Irish conifer plantations, and (ii) how survey characteristics influenced the precision of discrete-return metrics. A variance-based global sensitivity analysis demonstrated that discrete-return height distributions were accurately and consistently simulated across 100 forest inventory plots with few perturbations induced by varying acquisition parameters or ground topography. In contrast, discrete return density, canopy cover and the proportion of multiple returns were sensitive to fluctuations in sensor altitude, scanning angle, pulse repetition frequency and pulse duration. Our findings corroborate previous studies indicating that discrete-return heights are robust to varying acquisition parameters and may be reliable predictors for the indirect retrieval of forest inventory measurements. However, canopy cover and density metrics are only comparable for ALS data collected under similar acquisition conditions, precluding their universal use across different ALS surveys. Our study demonstrates that DART is a robust model for simulating discrete-return measurements over structurally complex forests; however, the replication of foliage morphology, density and orientation are important considerations for radiative transfer simulations using synthetic trees with explicitly defined crown architectures.
Modeling Polarized Reflectance of Natural Land Surfaces Using Generalized Regression Neural Networks
Authors: He Yuhao , Yang Bin , Lin Hui , Zhang Junqiang
Published: January 2020

Retrieval of complete aerosol properties over land through remote sensing requires accurate information about the polarization characteristics of natural land surfaces. In this paper, a new bidirectional polarization distribution function (BPDF) is proposed, using the generalized regression neural network (GRNN). This GRNN-based BPDF model builds a quite accurate nonlinear relationship between polarized reflectance and four input parameters, i.e., Fresnel factor, scattering angle, red, and near-infrared reflectances. It learns fast because only a smoothing parameter needs to be adjusted. The GRNN-based model is compared to six widely used BPDF models (i.e., Nadal–Bréon, Maignan, Waquet, Litivinov, Diner, and Xie–Cheng models), using the Polarization and Directionality of the Earth’s Reflectance (POLDER) measurements. Experiments suggest that the GRNN-based BPDF model is more accurate than these models. Compared with the best current models, the averaged root-mean-square error (RMSE) from the GRNN-based BPDF model can be reduced by 13.4% by using data collected during the whole year and is lower for 97.4% cases with data collected during every month. Moreover, compared to the widely used BPDF models, the GRNN-based BPDF model provides better performance when the scattering angle is small, and it is the first model that is able to reproduce negative polarized reflectance. The GRNN-based BPDF model is thus useful for the remote sensing of complete aerosol properties over land.
Algorithm with Patterned Singular Value Approach for Highly Reliable Autonomous Star Identification
Authors: Kim Kiduck , Bang Hyochoong
Published: January 2020

In the work reported in this paper, a lost-in-space star pattern identification algorithm for agile spacecraft was studied. Generally, the operation of a star tracker is known to exhibit serious degradation or even failure during fast attitude maneuvers. While tracking methods are widely used solutions to handle the dynamic conditions, they require prior information about the initial orientation. Therefore, the tracking methods may not be adequate for autonomy of attitude and control systems. In this paper a novel autonomous identification method for dynamic conditions is proposed. Additional constraints are taken into account that can significantly decrease the number of stars imaged and the centroid accuracy. A strategy combining two existing classes for star pattern identification is proposed. The new approach is intended to provide a unique way to determine the identity of stars that promises robustness against noise and rapid identification. Moreover, representative algorithms implemented in actual space applications were utilized as counterparts to analyze the performance of the proposed method in various scenarios. Numerical simulations show that the proposed method is not only highly robust against positional noise and false stars, but also guarantees fast run-time, which is appropriate for high-speed applications.
Two-Way Transmission for Low-Latency and High-Reliability 5G Cellular V2X Communications
Authors: Do Dinh-Thuan , Nguyen Tu-Trinh Thi , Le Chi-Bao , Lee Jeong Woo
Published: January 2020

As one of key technologies of future networks, vehicle-to-everything (V2X) communication has recently been proposed to improve conventional vehicle systems in terms of traffic and communications. Main benefits of using V2X are efficient and safe traffic as well as low-latency communications and reliable massive connections. Non-orthogonal multiple access (NOMA) scheme was introduced as a promising solution in the fifth-generation (5G) mobile communications, by which quality-of-service (QoS) requirements of many 5G-enabled applications are satisfied as a result of improved network throughput and lower accessing and transmission latency. In this paper, we study NOMA-based communications between vehicles equipped with multiple antennas over Nakagami-m fading channels in V2X networks, in which uplink and downlink transmission between two vehicles with upper controller are supported by a road side unit (RSU) to increase the capacity rather than simply be connected to the base station. In the NOMA-V2X system under study, the outage probability depends on the power allocation factor of RSU transmission and the operation of successive interference cancellation (SIC) at vehicles. Analyses and simulations verify that the outage performance of NOMA-V2X system are mainly affected by fading parameters, levels of imperfect SIC, and power allocation factors.
Refinement of TOA Localization with Sensor Position Uncertainty in Closed-Form
Authors: Gan Yi , Cong Xunchao , Sun Yimao
Published: January 2020

The subject of localization has received great deal attention in the past decades. Although it is perhaps a well-studied problem, there is still room for improvement. Traditional localization methods usually assume the number of sensors is sufficient for providing desired performance. However, this assumption is not always satisfied in practice. This paper studies the time of arrival (TOA)-based source positioning in the presence of sensor position errors. An error refined solution is developed for reducing the mean-squared-error (MSE) and bias in small sensor network (the number of sensors is fewer) when the noise or error level is relatively large. The MSE performance is analyzed theoretically and validated by simulations. Analytical and numerical results show the proposed method attains the Cramér-Rao lower bound (CRLB). It outperforms the existing closed-form methods with slightly raising computation complexity, especially in the larger noise/error case.
A Quantitative Method for Acesulfame K Using the Taste Sensor
Authors: Liu Yuanchang , Wu Xiao , Tahara Yusuke , Ikezaki Hidekazu , Toko Kiyoshi
Published: January 2020

We have developed a method to quantify the sweetness of negatively charged high-potency sweeteners coexisting with other taste substances. This kind of sweetness sensor uses lipid polymer membranes as the taste-sensing part. Two types of outputs have been defined in the measurement of the taste sensor: one is the relative value and the other is the CPA (the change in membrane potential caused by adsorption) value. The CPA value shows a good selectivity for high-potency sweeteners. On the other hand, the relative value is several times higher than the CPA value, but the relative value is influenced by salty substances. In order to obtain both high sensitivity and selectivity, we established a model for predicting the concentration of sweeteners with a nonlinear regression analysis method using the relative values of both the sweetness sensor and the saltiness sensor. The analysis results showed good correlations with the estimated concentration of acesulfame potassium coexisting with salty substances, as represented by R2 = 0.99. This model can correspond well to the prediction of acesulfame K in a concentration of 0.2–0.7 mM, which is commonly used in food and beverages. The results obtained in this paper suggest that this method is useful for the evaluation of acesulfame K using the taste sensors.
Analysis of the Key Factors Affecting the Capability and Optimization for Magnetostrictive Iron-Gallium Alloy Ambient Vibration Harvesters
Authors: Liu Huifang , Cong Chen , Cao Chongdong , Zhao Qiang
Published: January 2020

The basic phenomena of a cantilever energy harvesting device based on iron-gallium alloy magnetostrictive material for low frequency were systematically studied. The results highlighted how the physical parameters, geometric structure and bias conditions affected the vibration harvesting capacity through a thorough experimental aimed at enhancing the vibration energy harvesting capacity through an optimal design. How the performance is affected by the configuration of the multi-layers composite beam, material and dimensions of the elastic layer, arrangement position and number of bias magnets, the matching load resistance and other important design parameters was studied in depth. For the first time, it was clearly confirmed that the magnetic field of bias magnets and electromagnetic vibration shaker have almost no effect on the measurement of the voltage induced from the harvester. A harvesting power RMS up to 13.3 mW and power density RMS up to 3.7 mW/cm3/g was observed from the optimized prototype. Correspondingly, the DC output power and power density after the two-stage signal processing circuit were up to 5.2 mW and 1.45 mW/cm3/g, respectively. The prototype successfully powered multiple red light emitting diode lamps connected in a sinusoidal shape and multiple red digital display tubes, which verified the vibration harvesting capability or electricity-generating capability of the harvester prototype and the effectiveness of the signal converter.
Mechanical Energy before Chemical Energy at the Origins of Life?
Authors: Hansma
Published: January 2020

Mechanical forces and mechanical energy are prevalent in living cells. This may be because mechanical forces and mechanical energy preceded chemical energy at life’s origins. Mechanical energy is more readily available in non-living systems than the various forms of chemical energy used by living systems. Two possible prebiotic environments that might have provided mechanical energy are hot pools that experience wet/dry cycles and mica sheets as they move, open and shut, as heat pumps or in response to water movements.
Influence of Anthropogenic Noise for Predicting Cinereous Vulture Nest Distribution
Authors: Ortiz-Urbina Esther , Diaz-Balteiro Luis , Iglesias-Merchan Carlos
Published: January 2020

Natural landscapes are increasingly under anthropogenic pressures, and concern about human impacts on wildlife populations is becoming particularly relevant in the case of natural areas affected by roads. The expansion of road networks is considered among the main factors threatening biodiversity due to their potential for disturbing natural ecosystems on large scales. Indeed, traffic noise pollution reduces the quantity and the quality of natural habitats, and umbrella species are frequently used as indicators of natural ecosystem health. In this sense, there is a variety of GIS-based ecological modeling tools that allow evaluation of the factors that influence species distributions in order to accurately predict habitat selection. In this study, we have combined the use of noise modeling tools and maximum entropy modeling (MaxEnt) to evaluate the relative importance of environmental variables for Cinereous vulture (Aegypius monachus) nesting habitat selection within a mountainous forest in Spain. As a result, we found that spatial negative influence of roads on wildlife due to road traffic disturbance may have been traditionally overestimated when it has been inferred from distance measurements of wildlife behavior in road surroundings instead of from considering road traffic noise level exposure. In addition, we found a potential risk threshold for cinereous vulture breeding around roads, which ties in with a Leq24h level of 40 dB(A). This may be a useful indicator for assessing the potential impact of human activities on an umbrella species such as, for instance, the cinereous vulture, whose breeding does not take place where road traffic Leq24h levels are higher than 40 dB(A).
Patent Keyword Analysis of Disaster Artificial Intelligence Using Bayesian Network Modeling and Factor Analysis
Authors: Park Sangsung , Jun Sunghae
Published: January 2020

At present, artificial intelligence (AI) contributes to most technological fields. AI has also been introduced in the disaster area to replace humans and contribute to the prevention of disasters and the minimization of damages. So, it is necessary to analyze disaster AI in order to effectively make use of it. In this paper, we analyze the patent documents related to disaster AI technology. We propose Bayesian network modeling and factor analysis for the technology analysis of disaster AI. This is based on probability distribution and graph theory. It is also a statistical model that depends on multivariate data analysis. In order to show how the proposed model can be applied to a real problem, we carried out a case study to collect and analyze the patent data related to disaster AI.
A Systematic Review of Research on Sustainability in Mergers and Acquisitions
Authors: González-Torres Thais , Rodríguez-Sánchez José-Luis , Pelechano-Barahona Eva , García-Muiña Fernando E.
Published: January 2020

The field of mergers and acquisitions and sustainability has been expanding in the last years; consequently, there is a need for a systematic review of the existing body of knowledge. This article aims to fill this gap by providing an overview and synthesis of the research topic. The specific objectives are (1) to assess the academic impact, and (2) to determine the structure of the topic. To achieve these goals, a bibliometric analysis will be carried out through a selection of articles taken from the WoS database from 1900 to 2019. First, the historical evolution of publications will be addressed, the distribution of articles by journal and by individual author, as well as the total number of citations per article. Second, this article will develop networks of co-citations of authors and journals and co-word analysis by using VOS Viewer software. The global results will provide insights into the nature and trends of research on mergers and acquisitions and sustainability.
Exploring the Determinants of Food Security in the Areas of the Nam Theun2 Hydropower Project in Khammuan, Laos
Authors: Phami Phouvong , He Jianhua , Liu Dianfeng , Ding Su , Silva Patrik , Li Chun , Qin Zhijiao
Published: January 2020

This article examines the driving forces of food security in the areas of the Nam Theun2 Hydropower Project (NT2) in Khamuan, Laos. A questionnaire survey was conducted to collect data from 100 NT2 resettlement households based on the random sampling technique. A linear regression technique was used to identify the influence of household food insecurity. The result showed that household size, food price, drought, shock, household income per month, number of laborers, gender of the household head, and farmland areas are important factors for household food insecurity. Policies should focus on irrigation that will permit yearlong cultivation. This will in turn become the stimulus for a concatenation of events in the process of development. People will resettle to practice agriculture while also expanding non-agricultural employment. Businesses in skills training, fish processing, textile, services, and crafts will be created, boosting household income. With inevitable population expansion, education in family planning will also be necessary to control population in relation to available resources.
Construction Contract Administration Performance Assessment Tool by Using a Fuzzy Structural Equation Model
Authors: Gunduz Murat , Elsherbeny Hesham Ahmed
Published: January 2020

The contract administration process is inevitably complex, and improper performance of the associated tasks and procedures may lead to disputes between the contracting parties whilst further driving against the sustainability bottom lines. Therefore, this paper proposes an assessment tool to improve the implementation of construction contract administration (CCA) through a multi-dimensional construction contract administration performance model (CAPM), construction contract administration performance index (CCAPI), and a mobile software to assess the CCA performance at the project level by integrating the crisp value of the fuzzy set theory within a second-order confirmatory factor analysis of the structural equation modeling technique. A hybrid mobile application (CAPM) is developed using the Ionic framework which can run either in full model mode or short model mode with 93 and 33 key construction administration factors, respectively. Assessment of sustainability practices in the area of contract administration is a part of the holistic model and CAPM defines 13 major key indicators relevant to social and environmental sustainability while economic sustainability is scattered over the rest of the CAPM indicators. The CAPM is practically implemented in 13 international construction projects and the results reveal that the proposed tool is reasonably captured in the different performance levels of CCA, and we conclude a low level of implementing in risk management. As part of the holistic model, assessment of sustainability practices in the area of contract administration is separately discussed and the study reveals the need to improve the environmental and social sustainability practices in contract administration.
Legitimacy for Sustainability: A Case of A Strategy Change for An Oil and Gas Company
Authors: Jaber Tahrir , Oftedal Elin M.
Published: January 2020

This study introduces and validates a measure of a company’s institutional profile for sustainability. It uses institutional theory as a lens to understand the factors that legitimize the adoption of renewable energy activities in an oil and gas company. The three institutional dimensions used in this study are regulative, normative and cognitive, which aimed to measure legitimacy in an oil and gas company under a sustainable change. Survey-based research was carried out among employees to test this theory. Moreover, structural equation modeling was used to test the model fit, validate the measures, and test the four hypotheses. The results showed that regulative and normative pillars play the main role in legitimating renewable energy practices in our case company. The findings provide researchers and companies with a valuable resource for exploring legitimacy in order to understand what makes companies legitimize new sustainable activities that are outside the companies’ core business.
A Strategic Model for Sustainable Business Policy Development
Authors: Štrukelj Tjaša , Nikolić Jelena , Zlatanović Dejana , Sternad Zabukovšek Simona
Published: January 2020

Growing importance of sustainable development, corporate social responsibility and business ethics requires various types of contemporary organisations innovation. This research assesses the problem related to business policy innovation (BPI), which represents organisational governance determination. The main purpose of the paper is to qualitatively and quantitatively present a new, requisitely holistic strategic model of the soft factors influencing BPI, which interdependently incorporates changes in organisational values, culture and business ethics, as well as stakeholders’ interests reconciliation, thus determine soft possibilities for more sustainable business policy, management and practice. While the relevance of these factors for business policy is in the literature widely recognized, there is a small amount of empirical research on their influence on BPI. To mitigate this research gap, advanced structural equation modelling (SEM) based partial least squares (PLS) method was used for analysing data of 734 organisations in Slovenia, the EU state. The research results show that researched soft factors organisational values, culture and stakeholders’ interests reconciliation statistically confirmed influence BPI. Thus, these recognitions can be used as the basis for strategic managerial decision making towards social responsibility and sustainability of an organisation. Reasons why it has not been statistically confirmed that business ethics influence BPI needs to be investigated in future research.
Integrated Design Process for Modular Construction Projects to Reduce Rework
Authors: Hyun Hosang , Kim Hyunsoo , Lee Hyun-Soo , Park Moonseo , Lee Jeonghoon
Published: January 2020

In modular construction projects, unit production and onsite work are conducted concurrently, enabling shorter duration, lower cost, and improved quality. Because of the nature of the work, building design details should be determined early in the design phase, which requires information from participants. However, the design process for stick-built construction does not include such information, which leads to errors in design, such as omissions and conflicts of information from participants, causing reworking in the design phase. To reduce errors, an information flow should be identified representing when/what/how the information should be shared, and with whom. This paper proposes an integrated design process based on the information flow. To identify the flow, a precedence relationship between activities is represented using a dependency structure matrix (DSM). Then, the order of activities is rearranged using a partitioning algorithm. In this manner, unnecessary feedback and reverse information flow, which are related to errors, are reduced. Finally, the rearranged activities are proposed as an integrated design process. To validate the impact of the proposed process and methodology, interviews with experts were conducted. The validation results suggest that the project delivery method should also be considered in the early project phase in practical application.