bluuebunny/crossref_metadata_2025 huggingface dataset:
Hafnium vs. Zirconium, the Perpetual Battle for Supremacy in Catalytic Olefin Polymerization: A Simple Matter of Electrophilicity?
Authors: Vittoria Antonio , Goryunov Georgy P. , Izmer Vyatcheslav V. , Kononovich Dmitry S. , Samsonov Oleg V. , Zaccaria Francesco , Urciuoli Gaia , Budzelaar Peter H. M. , Busico Vincenzo , Voskoboynikov Alexander Z. , Uborsky Dmitry V. , Ehm Christian , Cipullo Roberta
Published: August 2021

The performance of C2-symmetric ansa-hafnocene catalysts for isotactic polypropylene typically deteriorates at increasing temperature much faster than that of their zirconium analogues. Herein, we analyze in detail a set of five Hf/Zr metallocene pairs—including some of the latest generation catalysts—at medium- to high-polymerization temperature. Quantitative structure–activity relationship (QSAR) models for stereoselectivity, the ratio allyl/vinyl chain ends, and 2,1/3,1 misinsertions in the polymer indicate a strong dependence of polymerization performance on electrophilicity of the catalyst, which is a function of the ligand framework and the metal center. Based on this insight, the stronger performance decline of hafnocenes is ascribed to electrophilicity-dependent stabilization effects.
Investigation of Electrical Properties of BiFeO3/LDPE Nanocomposite Dielectrics with Magnetization Treatments
Authors: Song Wei , Sun Yu , Yu Tian-Jiao , Fan Yu-Zhang , Sun Zhi , Han Bai
Published: August 2021

The purpose of this paper is to study the effect of nano-bismuth ferrite (BiFeO3) on the electrical properties of low-density polyethylene (LDPE) under magnetic-field treatment at different temperatures. BiFeO3/LDPE nanocomposites with 2% mass fraction were prepared by the melt-blending method, and their electrical properties were studied. The results showed that compared with LDPE alone, nanocomposites increased the crystal concentration of LDPE and the spherulites of LDPE. Filamentous flake aggregates could be observed. The spherulite change was more obvious under high-temperature magnetization. An agglomerate phenomenon appeared in the composite, and the particle distribution was clear. Under high-temperature magnetization, BiFeO3 particles were increased and showed a certain order, but the change for room-temperature magnetization was not obvious. The addition of BiFeO3 increased the crystallinity of LDPE. Although the crystallinity decreased after magnetization, it was higher than that of LDPE. An AC test showed that the breakdown strength of the composite was higher than that of LDPE. The breakdown strength increased after magnetization. The increase of breakdown strength at high temperature was less, but the breakdown field strength of the composite was higher than that of LDPE. Compared with LDPE, the conductive current of the composite was lower. So, adding BiFeO3 could improve the dielectric properties of LDPE. The current of the composite decayed faster with time. The current decayed slowly after magnetization.
Adhesive Performance of Acrylic Pressure-Sensitive Adhesives from Different Preparation Processes
Authors: Márquez Irene , Paredes Núria , Alarcia Felipe , Velasco José Ignacio
Published: August 2021

A series of pressure-sensitive adhesives (PSAs) was prepared using a constant monomeric composition and different preparation processes to investigate the best combination to obtain the best balance between peel resistance, tack, and shear resistance. The monomeric composition was a 1:1 combination of two different water-based acrylic polymers—one with a high shear resistance (A) and the other with a high peel resistance and tack (B). Two different strategies were applied to prepare the adhesives: physical blending of polymers A and B and in situ emulsion polymerization of A + B, either in one or two steps; in this last case, by polymerizing A or B first. To characterize the polymer, the average particle size and viscosity were analyzed. The glass transition temperature (Tg) was determined by differential scanning calorimetry (DSC). The tetrahydrofuran (THF) insoluble polymer fraction was used to calculate the gel content, and the soluble part was used to determine the average sol molecular weight by means of gel permeation chromatography (GPC). The adhesive performance was assessed by measuring tack as well as peel and shear resistance. The mechanical properties were obtained by calculating the shear modulus and determination of maximum stress and the deformation energy. Moreover, an adhesive performance index (API) was designed to determine which samples are closest to the requirements demanded by the self-adhesive label market.
Aminated Graphene-Graft-Oligo(Glutamic Acid) /Poly(ε-Caprolactone) Composites: Preparation, Characterization and Biological Evaluation
Authors: Stepanova Mariia , Solomakha Olga , Rabchinskii Maxim , Averianov Ilia , Gofman Iosif , Nashchekina Yuliya , Antonov Grigorii , Smirnov Aleksey , Ber Boris , Nashchekin Aleksey , Korzhikova-Vlakh Evgenia
Published: August 2021

Biodegradable and biocompatible composites are of great interest as biomedical materials for various regeneration processes such as the regeneration of bones, cartilage and soft tissues. Modification of the filler surface can improve its compatibility with the polymer matrix, and, as a result, the characteristics and properties of composite materials. This work is devoted to the synthesis and modification of aminated graphene with oligomers of glutamic acid and their use for the preparation of composite materials based on poly(ε-caprolactone). Ring-opening polymerization of N-carboxyanhydride of glutamic acid γ-benzyl ester was used to graft oligomers of glutamic acid from the surface of aminated graphene. The success of the modification was confirmed by Fourier-transform infrared and X-ray photoelectron spectroscopy as well as thermogravimetric analysis. In addition, the dispersions of neat and modified aminated graphene were analyzed by dynamic and electrophoretic light scattering to monitor changes in the characteristics due to modification. The poly(ε-caprolactone) films filled with neat and modified aminated graphene were manufactured and carefully characterized for their mechanical and biological properties. Grafting of glutamic acid oligomers from the surface of aminated graphene improved the distribution of the filler in the polymer matrix that, in turn, positively affected the mechanical properties of composite materials in comparison to ones containing the unmodified filler. Moreover, the modification improved the biocompatibility of the filler with human MG-63 osteoblast-like cells.
Fabrication and Photocatalytic Properties of Electrospun Fe-Doped TiO2 Nanofibers Using Polyvinyl Pyrrolidone Precursors
Authors: Na Kyeong-Han , Kim Bo-Sung , Yoon Han-Sol , Song Tae-Hyeob , Kim Sung-Wook , Cho Churl-Hee , Choi Won-Youl
Published: August 2021

For the removal of pollutants, a modified TiO2 photocatalyst is attracting attention. Fe-doped TiO2 nanofibers were prepared through a combination of electrospinning and calcination. Morphological characterization of the sample was conducted using field-emission scanning electron and transmission electron microscopy. The crystal structure of each sample was analyzed using high-resolution transmission electron microscopy, selected area electron diffraction, and Fast Fourier Transform imaging. The average diameter of the Fe-doped TiO2 nanofibers was measured to be 161.5 nm and that of the pure TiO2 nanofibers was 181.5 nm. The crystal phase when heat treated at 350 °C was anatase for TiO2 nanofibers and rutile for Fe-doped TiO2 nanofibers. The crystal phase of the TiO2 matrix was easily transitioned to rutile by Fe-doping. The photocatalytic performance of each sample was compared via the photodegradation of methylene blue and acid orange 7 under ultraviolet and visible light irradiation. In the Fe-doped TiO2 nanofibers, photodegradation rates of 38.3% and 27.9% were measured under UV irradiation and visible light, respectively. Although other catalysts were not activated, the photodegradation rate in the Fe-doped TiO2 nanofibers was 9.6% using acid orange 7 and visible light. For improved photocatalytic activity, it is necessary to study the concentration control of the Fe dopant.
Tissue-Engineered Carotid Artery Interposition Grafts Demonstrate High Primary Patency and Promote Vascular Tissue Regeneration in the Ovine Model
Authors: Antonova Larisa V. , Krivkina Evgenia O. , Sevostianova Viktoriia V. , Mironov Andrey V. , Rezvova Maria A. , Shabaev Amin R. , Tkachenko Vadim O. , Krutitskiy Sergey S. , Khanova Mariam Yu. , Sergeeva Tatiana Yu. , Matveeva Vera G. , Glushkova Tatiana V. , Kutikhin Anton G. , Mukhamadiyarov Rinat A. , Deeva Nadezhda S. , Akentieva Tatiana N. , Sinitsky Maxim Yu. , Velikanova Elena A. , Barbarash Leonid S.
Published: August 2021

Tissue-engineered vascular graft for the reconstruction of small arteries is still an unmet clinical need, despite the fact that a number of promising prototypes have entered preclinical development. Here we test Poly(3-hydroxybutyrate-co-3-hydroxyvalerate)Poly(ε-caprolactone) 4-mm-diameter vascular grafts equipped with vascular endothelial growth factor (VEGF), basic fibroblast growth factor (bFGF) and stromal cell-derived factor 1α (SDF-1α) and surface coated with heparin and iloprost (PHBV/PCL[VEGF-bFGF-SDF]Hep/Ilo, n = 8) in a sheep carotid artery interposition model, using biostable vascular prostheses of expanded poly(tetrafluoroethylene) (ePTFE, n = 5) as a control. Primary patency of PHBV/PCL[VEGF-bFGF-SDF]Hep/Ilo grafts was 62.5% (5/8) at 24 h postimplantation and 50% (4/8) at 18 months postimplantation, while all (5/5) ePTFE conduits were occluded within the 24 h after the surgery. At 18 months postimplantation, PHBV/PCL[VEGF-bFGF-SDF]Hep/Ilo grafts were completely resorbed and replaced by the vascular tissue. Regenerated arteries displayed a hierarchical three-layer structure similar to the native blood vessels, being fully endothelialised, highly vascularised and populated by vascular smooth muscle cells and macrophages. The most (4/5, 80%) of the regenerated arteries were free of calcifications but suffered from the aneurysmatic dilation. Therefore, biodegradable PHBV/PCL[VEGF-bFGF-SDF]Hep/Ilo grafts showed better short- and long-term results than bio-stable ePTFE analogues, although these scaffolds must be reinforced for the efficient prevention of aneurysms.
Binary Graft of Poly(N-vinylcaprolactam) and Poly(acrylic acid) onto Chitosan Hydrogels Using Ionizing Radiation for the Retention and Controlled Release of Therapeutic Compounds
Authors: Ortega Alejandra , Sánchez Abigail , Burillo Guillermina
Published: August 2021

In this study, we carried out the synthesis of a thermo- and pH-sensitive binary graft, based on N-vinylcaprolactam (NVCL) and pH sensitive acrylic acid (AAc) monomers, onto chitosan gels (net-CS) by ionizing radiation. Pre-oxidative irradiation and direct methods were examined, and materials obtained were characterized by FTIR-ATR, thermogravimetric analysis (TGA), scanning electron microscopy (SEM), and swelling tests (equilibrium swelling time, critical pH, and temperature). The best synthesis radiation method was the direct method, which resulted in the maximum grafting percentages (~40%) at low doses (10–12 kGy). The main goal of this study was the comparison of the swelling behavior and physicochemical properties of net-CS with those of the binary system (net-CS)-g-NVCL/AAc with the optimum grafting percentage (~30%). This produced a material that showed an upper critical solution temperature (UCST) of 33.5 °C and a critical pH value of 3.8, indicating the system is more hydrophilic at higher temperatures and low pH values. Load and release studies were carried out using diclofenac. The grafted system (32%) was able to load 19.3 mg g−1 of diclofenac and release about 95% within 200 min, in comparison to net-CS, which only released 80% during the same period. When the grafted system was protonated before diclofenac loading, it loaded 27.6 mg g−1. However, the drug was strongly retained in the material by electrostatic interactions and only released about 20%.
Current Insights into Collagen Type I
Authors: Naomi Ruth , Ridzuan Pauzi Muhd , Bahari Hasnah
Published: August 2021

Collagen type I (Col-I) is unique due to its high biocompatibility in human tissue. Despite its availability from various sources, Col-I naturally mimics the extracellular matrix (ECM) and generally makes up the larger protein component (90%) in vasculature, skin, tendon bone, and other tissue. The acceptable physicochemical properties of native Col-I further enhance the incorporation of Col-I in various fields, including pharmaceutical, cosmeceutical, regenerative medicine, and clinical. This review aims to discuss Col-I, covering the structure, various sources of availability, native collagen synthesis, current extraction methods, physicochemical characteristics, applications in various fields, and biomarkers. The review is intended to provide specific information on Col-I currently available, going back five years. This is expected to provide a helping hand for researchers who are concerned about any development on collagen-based products particularly for therapeutic fields.
Effect of Different Pretreatments on Sludge Solubilization and Estimation of Bioenergy Potential
Authors: Babu Reshma , Capannelli Gustavo , Comite Antonio
Published: August 2021

Most of the conventional treatments of waste-activated sludge (WAS) are devoted to their minimization and destruction. On the other hand, the biomass contained in WAS can be utilized as a valuable source of renewable carbon. In this study, the influence of different pretreatments (ultrasonication, chemical, thermal, and combined pretreatments) was explored for sludge solubilization. Effects of the pretreatments were investigated as a function of the solubilization of total solids (TS), volatile solids (VS), and chemical oxygen demand (COD). Concentrations of soluble carbohydrates and total nitrogen were also measured. The most effective pretreatment to hydrolyze sludge was found to be the combined alkali–thermal (pH 12, 75 °C) pretreatment method, leading to TS and vs. solubilization of 9.6% and 17.2%, respectively. Soluble COD, carbohydrates, total nitrogen, and proteins estimated in the liquid phase were 5235 mg/L, 732 mg/L, 430 mg/L, and 2688 mg/L, respectively. Thus, the alkali–thermal method could be used for efficient valorization of WAS. Moreover, the solid fraction from all pretreated samples was further subjected to thermogravimetric analysis to estimate its potential for bioenergy from its higher heating value (HHV), which was found to be in the range of 10–11.82 MJ/kg. This study can provide better insight into the efficient valorization of liquid and solid phases of sludge after pretreatment.
Lithology Classification Using TASI Thermal Infrared Hyperspectral Data with Convolutional Neural Networks
Authors: Liu Huize , Wu Ke , Xu Honggen , Xu Ying
Published: August 2021

In recent decades, lithological mapping techniques using hyperspectral remotely sensed imagery have developed rapidly. The processing chains using visible-near infrared (VNIR) and shortwave infrared (SWIR) hyperspectral data are proven to be available in practice. The thermal infrared (TIR) portion of the electromagnetic spectrum has considerable potential for mineral and lithology mapping. In particular, the abovementioned rocks at wavelengths of 8–12 μm were found to be discriminative, which can be seen as a characteristic to apply to lithology classification. Moreover, it was found that most of the lithology mapping and classification for hyperspectral thermal infrared data are still carried out by traditional spectral matching methods, which are not very reliable due to the complex diversity of geological lithology. In recent years, deep learning has made great achievements in hyperspectral imagery classification feature extraction. It usually captures abstract features through a multilayer network, especially convolutional neural networks (CNNs), which have received more attention due to their unique advantages. Hence, in this paper, lithology classification with CNNs was tested on thermal infrared hyperspectral data using a Thermal Airborne Spectrographic Imager (TASI) at three small sites in Liuyuan, Gansu Province, China. Three different CNN algorithms, including one-dimensional CNN (1-D CNN), two-dimensional CNN (2-D CNN) and three-dimensional CNN (3-D CNN), were implemented and compared to the six relevant state-of-the-art methods. At the three sites, the maximum overall accuracy (OA) based on CNNs was 94.70%, 96.47% and 98.56%, representing improvements of 22.58%, 25.93% and 16.88% over the worst OA. Meanwhile, the average accuracy of all classes (AA) and kappa coefficient (kappa) value were consistent with the OA, which confirmed that the focal method effectively improved accuracy and outperformed other methods.
Estimation of Evapotranspiration and Its Components across China Based on a Modified Priestley–Taylor Algorithm Using Monthly Multi-Layer Soil Moisture Data
Authors: Xing Wanqiu , Wang Weiguang , Shao Quanxi , Song Linye , Cao Mingzhu
Published: August 2021

Although soil moisture (SM) is an important constraint factor of evapotranspiration (ET), the majority of the satellite-driven ET models do not include SM observations, especially the SM at different depths, since its spatial and temporal distribution is difficult to obtain. Based on monthly three-layer SM data at a 0.25° spatial resolution determined from multi-sources, we updated the original Priestley Taylor–Jet Propulsion Laboratory (PT-JPL) algorithm to the Priestley Taylor–Soil Moisture Evapotranspiration (PT-SM ET) algorithm by incorporating SM control into soil evaporation (Es) and canopy transpiration (T). Both algorithms were evaluated using 17 eddy covariance towers across different biomes of China. The PT-SM ET model shows increased R2, NSE and reduced RMSE, Bias, with more improvements occurring in water-limited regions. SM incorporation into T enhanced ET estimates by increasing R2 and NSE by 4% and 18%, respectively, and RMSE and Bias were respectively reduced by 34% and 7 mm. Moreover, we applied the two ET algorithms to the whole of China and found larger increases in T and Es in the central, northeastern, and southern regions of China when using the PT-SM algorithm compared with the original algorithm. Additionally, the estimated mean annual ET increased from the northwest to the southeast. The SM constraint resulted in higher transpiration estimate and lower evaporation estimate. Es was greatest in the northwest arid region, interception was a large fraction in some rainforests, and T was dominant in most other regions. Further improvements in the estimation of ET components at high spatial and temporal resolution are likely to lead to a better understanding of the water movement through the soil–plant–atmosphere continuum.
MSResNet: Multiscale Residual Network via Self-Supervised Learning for Water-Body Detection in Remote Sensing Imagery
Authors: Dang Bo , Li Yansheng
Published: August 2021

Driven by the urgent demand for flood monitoring, water resource management and environmental protection, water-body detection in remote sensing imagery has attracted increasing research attention. Deep semantic segmentation networks (DSSNs) have gradually become the mainstream technology used for remote sensing image water-body detection, but two vital problems remain. One problem is that the traditional structure of DSSNs does not consider multiscale and multishape characteristics of water bodies. Another problem is that a large amount of unlabeled data is not fully utilized during the training process, but the unlabeled data often contain meaningful supervision information. In this paper, we propose a novel multiscale residual network (MSResNet) that uses self-supervised learning (SSL) for water-body detection. More specifically, our well-designed MSResNet distinguishes water bodies with different scales and shapes and helps retain the detailed boundaries of water bodies. In addition, the optimization of MSResNet with our SSL strategy can improve the stability and universality of the method, and the presented SSL approach can be flexibly extended to practical applications. Extensive experiments on two publicly open datasets, including the 2020 Gaofen Challenge water-body segmentation dataset and the GID dataset, demonstrate that our MSResNet can obviously outperform state-of-the-art deep learning backbones and that our SSL strategy can further improve the water-body detection performance.
Radiometric Normalization for Cross-Sensor Optical Gaofen Images with Change Detection and Chi-Square Test
Authors: Yan Li , Yang Jianbing , Zhang Yi , Zhao Anqi , Li Xi
Published: August 2021

As the number of cross-sensor images increases continuously, the surface reflectance of these images is inconsistent at the same ground objects due to different revisit periods and swaths. The surface reflectance consistency between cross-sensor images determines the accuracy of change detection, classification, and land surface parameter inversion, which is the most widespread application. We proposed a relative radiometric normalization (RRN) method to improve the surface reflectance consistency based on the change detection and chi-square test. The main contribution was that a novel chi-square test automatically extracts the stably unchanged samples between the reference and subject images from the unchanged regions detected by the change-detection method. We used the cross-senor optical images of Gaofen-1 and Gaofen-2 to test this method and four metrics to quantitatively evaluate the RRN performance, including the Root Mean Square Error (RMSE), spectral angle cosine, structural similarity, and CIEDE2000 color difference. Four metrics demonstrate the effectiveness of our proposed RRN method, especially the reduced percentage of RMSE after normalization was more than 80%. Comparing the radiometric differences of five ground features, the surface reflectance curve of two Gaofen images showed more minor differences after normalization, and the RMSE was smaller than 50 with the reduced percentages of about 50–80%. Moreover, the unchanged feature regions are detected by the change-detection method from the bitemporal Sentinel-2 images, which can be used for RRN without detecting changes in subject images. In addition, extracting samples with the chi-square test can effectively improve the surface reflectance consistency.
Multi-Bernoulli Tracking Approach for Occupancy Monitoring of Smart Buildings Using Low-Resolution Infrared Sensor Array
Authors: Rabiee Ramtin , Karlsson Johannes
Published: August 2021

Knowledge about the indoor occupancy is one of the important sources of information to design smart buildings. In some applications, the number of occupants in each zone is required. However, there are many challenges such as user privacy, communication limit, and sensor’s computational capability in development of the occupancy monitoring systems. In this work, a people flow counting algorithm has been developed which uses low-resolution thermal images to avoid any privacy concern. Moreover, the proposed scheme is designed to be applicable for wireless sensor networks based on the internet-of-things platform. Simple low-complexity image processing techniques are considered to detect possible objects in sensor’s field of view. To tackle the noisy detection measurements, a multi-Bernoulli target tracking approach is used to track and finally to count the number of people passing the area of interest in different directions. Based on the sensor node’s processing capability, one can consider either a centralized or a full in situ people flow counting system. By performing the tracking part either in sensor node or in a fusion center, there would be a trade off between the computational complexity and the transmission rate. Therefore, the developed system can be performed in a wide range of applications with different processing and transmission constraints. The accuracy and robustness of the proposed method are also evaluated with real measurements from different conducted trials and open-source dataset.
PolSAR Image Land Cover Classification Based on Hierarchical Capsule Network
Authors: Cheng Jianda , Zhang Fan , Xiang Deliang , Yin Qiang , Zhou Yongsheng , Wang Wei
Published: August 2021

Polarimetric synthetic aperture radar (PolSAR) image classification is one of the basic methods of PolSAR image interpretation. Deep learning algorithms, especially convolutional neural networks (CNNs), have been widely used in PolSAR image classification due to their powerful feature learning capabilities. However, a single neuron in the CNN cannot represent multiple polarimetric attributes of the land cover. The capsule network (CapsNet) uses vectors instead of the single neuron to characterize the polarimetric attributes, which improves the classification performance compared with traditional CNNs. In this paper, a hierarchical capsule network (HCapsNet) is proposed for the land cover classification of PolSAR images, which can consider the deep features obtained at different network levels in the classification. Moreover, we adopt three attributes to uniformly describe the scattering mechanisms of different land covers: phase, amplitude, and polarimetric decomposition parameters, which improves the generalization performance of HCapsNet. Furthermore, conditional random field (CRF) is added to the classification framework to eliminate small isolated regions of the intra-class. Comprehensive evaluations are performed on three PolSAR datasets acquired by different sensors, which demonstrate that our proposed method outperforms other state-of-the-art methods.
Missing the Forest and the Trees: Utility, Limits and Caveats for Drone Imaging of Coastal Marine Ecosystems
Authors: Tait Leigh W. , Orchard Shane , Schiel David R.
Published: August 2021

Coastal marine ecosystems are under stress, yet actionable information about the cumulative effects of human impacts has eluded ecologists. Habitat-forming seaweeds in temperate regions provide myriad irreplaceable ecosystem services, but they are increasingly at risk of local and regional extinction from extreme climatic events and the cumulative impacts of land-use change and extractive activities. Informing appropriate management strategies to reduce the impacts of stressors requires comprehensive knowledge of species diversity, abundance and distributions. Remote sensing undoubtedly provides answers, but collecting imagery at appropriate resolution and spatial extent, and then accurately and precisely validating these datasets is not straightforward. Comprehensive and long-running monitoring of rocky reefs exist globally but are often limited to a small subset of reef platforms readily accessible to in-situ studies. Key vulnerable habitat-forming seaweeds are often not well-assessed by traditional in-situ methods, nor are they well-captured by passive remote sensing by satellites. Here we describe the utility of drone-based methods for monitoring and detecting key rocky intertidal habitat types, the limitations and caveats of these methods, and suggest a standardised workflow for achieving consistent results that will fulfil the needs of managers for conservation efforts.
Improvements of ADAM3 by Incorporating New Dust Emission Reduction Formulations Based on Real-Time MODIS NDVI
Authors: Cho Jeong Hoon , Ryoo Sang-Boom , Kim Jinwon
Published: August 2021

Dust events in Northeast Asia have several adverse effects on human health, agricultural land, infrastructure, and transport. Wind speed is the most important factor in determining the total dust emission at the land surface; however, various land-surface conditions must be considered as well. Recently, the Korea Meteorological Administration updated the dust emission reduction factor (RF) in the Asian Dust Aerosol Model 3 (ADAM3) using data from the normalized difference vegetation index (NDVI) of the Moderate Resolution Imaging Spectroradiometer (MODIS). We evaluated the improvements of ADAM3 according to soil types. We incorporated new RF formulations in the evaluation based on real-time MODIS NDVI data obtained over the Asian dust source regions in northern China during spring 2017. This incorporation improved the simulation performance of ADAM3 for the PM10 mass concentration in Inner Mongolia and Manchuria for all soil types, except Gobi. The ADAM3 skill scores for sand, loess, and mixed types in a 24 h forecast increased by 6.6%, 20.4%, and 13.3%, respectively, compared with those in forecasts employing the monthly RF based on the NDVI data. As surface conditions in the dust source regions continually change, incorporating real-time vegetation data is critical to improving performance of dust forecast models such as ADAM3.
Estimation of Soil Organic Carbon Contents in Croplands of Bavaria from SCMaP Soil Reflectance Composites
Authors: Zepp Simone , Heiden Uta , Bachmann Martin , Wiesmeier Martin , Steininger Michael , van Wesemael Bas
Published: August 2021

For food security issues or global climate change, there is a growing need for large-scale knowledge of soil organic carbon (SOC) contents in agricultural soils. To capture and quantify SOC contents at a field scale, Earth Observation (EO) can be a valuable data source for area-wide mapping. The extraction of exposed soils from EO data is challenging due to temporal or permanent vegetation cover, the influence of soil moisture or the condition of the soil surface. Compositing techniques of multitemporal satellite images provide an alternative to retrieve exposed soils and to produce a data source. The repeatable soil composites, containing averaged exposed soil areas over several years, are relatively independent from seasonal soil moisture and surface conditions and provide a new EO-based data source that can be used to estimate SOC contents over large geographical areas with a high spatial resolution. Here, we applied the Soil Composite Mapping Processor (SCMaP) to the Landsat archive between 1984 and 2014 of images covering Bavaria, Germany. Compared to existing SOC modeling approaches based on single scenes, the 30-year SCMaP soil reflectance composite (SRC) with a spatial resolution of 30 m is used. The SRC spectral information is correlated with point soil data using different machine learning algorithms to estimate the SOC contents in cropland topsoils of Bavaria. We developed a pre-processing technique to address the issue of combining point information with EO pixels for the purpose of modeling. We applied different modeling methods often used in EO soil studies to choose the best SOC prediction model. Based on the model accuracies and performances, the Random Forest (RF) showed the best capabilities to predict the SOC contents in Bavaria (R² = 0.67, RMSE = 1.24%, RPD = 1.77, CCC = 0.78). We further validated the model results with an independent dataset. The comparison between the measured and predicted SOC contents showed a mean difference of 0.11% SOC using the best RF model. The SCMaP SRC is a promising approach to predict the spatial SOC distribution over large geographical extents with a high spatial resolution (30 m).
Comparative Analysis on the Estimation of Diurnal Solar-Induced Chlorophyll Fluorescence Dynamics for a Subtropical Evergreen Coniferous Forest
Authors: Chen Jinghua , Wang Shaoqiang , Chen Bin , Li Yue , Amir Muhammad , Ma Li , Zhu Kai , Yang Fengting , Wang Xiaobo , Liu Yuanyuan , Wang Pengyuan , Wang Junbang , Huang Mei , Wang Zhaosheng
Published: August 2021

Solar-induced chlorophyll fluorescence (SIF) is considered as a prospective indicator of vegetation photosynthetic activity and the ecosystem carbon cycle. The current coarse spatial-temporal resolutions of SIF data from satellite missions and ground measurements still cannot satisfy the corroboration of its correlation with photosynthesis and carbon flux. Practical approaches are needed to be explored for the supplementation of the SIF measurements. In our study, we clarified the diurnal variations of leaf and canopy chlorophyll fluorescence for a subtropical evergreen coniferous forest and evaluated the performance of the canopy chlorophyll concentration (CCC) approach and the backward approach from gross primary production (GPP) for estimating the diurnal variations of canopy SIF by comparing with the Soil Canopy Observation Photosynthesis Energy (SCOPE) model. The results showed that the canopy SIF had similar seasonal and diurnal variations with the incident photosynthetically active radiation (PAR) above the canopy, while the leaf steady-state fluorescence remained stable during the daytime. Neither the CCC nor the raw backward approach from GPP could capture the short temporal dynamics of canopy SIF. However, after improving the backward approach with a correction factor of normalized PAR incident on leaves, the variation of the estimated canopy SIF accounted for more than half of the diurnal variations in the canopy SIF (SIF687: R2 = 0.53, p < 0.001; SIF760: R2 = 0.72, p < 0.001) for the subtropical evergreen coniferous forest without water stress. Drought interfered with the utilization of the improved backward approach because of the decoupling of SIF and GPP due to stomatal closure. This new approach offers new insight into the estimation of diurnal canopy SIF and can help understand the photosynthesis of vegetation for future climate change studies.
Critical Points Extraction from Building Façades by Analyzing Gradient Structure Tensor
Authors: Chen Dong , Li Jing , Di Shaoning , Peethambaran Jiju , Xiang Guiqiu , Wan Lincheng , Li Xianghong
Published: August 2021

This paper proposes a building façade contouring method from LiDAR (Light Detection and Ranging) scans and photogrammetric point clouds. To this end, we calculate the confidence property at multiple scales for an individual point cloud to measure the point cloud’s quality. The confidence property is utilized in the definition of the gradient for each point. We encode the individual point gradient structure tensor, whose eigenvalues reflect the gradient variations in the local neighborhood areas. The critical point clouds representing the building façade and rooftop (if, of course, such rooftops exist) contours are then extracted by jointly analyzing dual-thresholds of the gradient and gradient structure tensor. Based on the requirements of compact representation, the initial obtained critical points are finally downsampled, thereby achieving a tradeoff between the accurate geometry and abstract representation at a reasonable level. Various experiments using representative buildings in Semantic3D benchmark and other ubiquitous point clouds from ALS DublinCity and Dutch AHN3 datasets, MLS TerraMobilita/iQmulus 3D urban analysis benchmark, UAV-based photogrammetric dataset, and GeoSLAM ZEB-HORIZON scans have shown that the proposed method generates building contours that are accurate, lightweight, and robust to ubiquitous point clouds. Two comparison experiments also prove the superiority of the proposed method in terms of topological correctness, geometric accuracy, and representation compactness.
Gated Autoencoder Network for Spectral–Spatial Hyperspectral Unmixing
Authors: Hua Ziqiang , Li Xiaorun , Jiang Jianfeng , Zhao Liaoying
Published: August 2021

Convolution-based autoencoder networks have yielded promising performances in exploiting spatial–contextual signatures for spectral unmixing. However, the extracted spectral and spatial features of some networks are aggregated, which makes it difficult to balance their effects on unmixing results. In this paper, we propose two gated autoencoder networks with the intention of adaptively controlling the contribution of spectral and spatial features in unmixing process. Gating mechanism is adopted in the networks to filter and regularize spatial features to construct an unmixing algorithm based on spectral information and supplemented by spatial information. In addition, abundance sparsity regularization and gating regularization are introduced to ensure the appropriate implementation. Experimental results validate the superiority of the proposed method to the state-of-the-art techniques in both synthetic and real-world scenes. This study confirms the effectiveness of gating mechanism in improving the accuracy and efficiency of utilizing spatial signatures for spectral unmixing.
Towards Smart City Governance. Case Study: Improving the Interpretation of Quantitative Traffic Measurement Data through Citizen Participation
Authors: Fonseca David , Sanchez-Sepulveda Monica , Necchi Silvia , Peña Enric
Published: August 2021

Citizens play a core role in sustainable cities as users of the services delivered by cities and as active participants in initiatives aimed at making cities more sustainable. This paper considers the role of citizens as information providers and discusses the conditions under which citizens can participate in the development of sustainable cities. The objective of this study is to document the sustainability of an urban transit system and evaluate its compliance, with citizen participation as a major contributor. The methodology used is intensive field visits, interviews, and a mixed analysis of Sant Andreu de Palomar District in Barcelona city. The circulating vehicles are quantitatively monitored, qualitative problems are detected, and the typology of vehicles and other aspects identified and detailed in the study are indicated. All this information is contrasted with that of the technological sensors in the sectors. The results indicate that vehicles in the current pattern of urban density planned under incorrect sensor operation influence sustainable behavior through agglomerative clustering. This paper provides recommendations for future urban sustainability assessment research, including the employment of mixed-methods research, among other strategies. This article is intended to assist policymakers and traffic engineers in evaluating the sustainability of urban transportation infrastructure projects considering citizens as sensors.
Determination of Motion Parameters of Selected Major Tectonic Plates Based on GNSS Station Positions and Velocities in the ITRF2014
Authors: Jagoda Marcin
Published: August 2021

Current knowledge about tectonic plate movement is widely applied in numerous scientific fields; however, questions still remain to be answered. In this study, the focus is on the determination and analysis of the parameters that describe tectonic plate movement, i.e., the position (Φ and Λ) of the rotation pole and angular rotation speed (ω). The study was based on observational material, namely the positions and velocities of the GNSS stations in the International Terrestrial Reference Frame 2014 (ITRF2014), and based on these data, the motion parameters of five major tectonic plates were determined. All calculations were performed using software based on a least squares adjustment procedure that was developed by the author. The following results were obtained: for the African plate, Φ = 49.15 ± 0.10°, Λ = −80.82 ± 0.30°, and ω = 0.267 ± 0.001°/Ma; for the Australian plate, Φ = 32.94 ± 0.05°, Λ = 37.70 ± 0.12°, and ω = 0.624 ± 0.001°/Ma; for the South American plate, Φ = –19.03 ± 0.20°, Λ = −119.78 ± 0.39°, and ω = 0.117 ± 0.001°/Ma; for the Pacific plate, Φ = −62.45 ± 0.07°, Λ = 111.01 ± 0.14°, and ω = 0.667 ± 0.001°/Ma; and for the Antarctic plate, Φ = 61.54 ± 0.30°, Λ = −123.01 ± 0.49°, and ω = 0.241 ± 0.003°/Ma. Then, the results were compared with the geological plate motion model NNR-MORVEL56 and the geodetic model ITRF2014 PMM, with good agreement. In the study, a new approach is proposed for determining plate motion parameters, namely the sequential method. This method allows one to optimize the data by determining the minimum number of stations required for a stable solution and by identifying the stations that negatively affect the quality of the solution and increase the formal errors of the determined parameters. It was found that the stability of the solutions of the Φ, Λ, and ω parameters varied depending on the parameters and the individual tectonic plates.
Upgrading Behavioral Models for the Design of Digital Predistorters
Authors: Crespo-Cadenas Carlos , Madero-Ayora María José , Becerra Juan A.
Published: August 2021

This work presents a strategy to upgrade models for power amplifier (PA) behavioral modeling and digital predistortion (DPD). These incomplete structures are the consequence of nonlinear order and memory depth model truncation with the purpose of reducing the demand of the limited computational resources available in standard processors. On the other hand, the alternative use of model structures pruned a priori does not guarantee that every significant term is included. To improve the limited performance of an incomplete model, a general procedure to augment its structure by incorporating significant terms is demonstrated. The sparse nature of the problem allows a successive search incorporating additional terms with higher nonlinear order and memory depth. This approach is investigated in the modeling and linearization of a commercial class AB PA operating at a compression point of about 6 dB, and a class J PA operating near saturation. Results highlight the capabilities of this upgrading procedure in the improvement of linearization capabilities of DPDs.
An Efficient Turbo Decoding and Frequency Domain Turbo Equalization for LTE Based Narrowband Internet of Things (NB-IoT) Systems
Authors: Adamu Mohammed Jajere , Qiang Li , Zakariyya Rabiu Sale , Nyatega Charles Okanda , Kawuwa Halima Bello , Younis Ayesha
Published: August 2021

This paper addresses the main crucial aspects of physical (PHY) layer channel coding in uplink NB-IoT systems. In uplink NB-IoT systems, various channel coding algorithms are deployed due to the nature of the adopted Long-Term Evolution (LTE) channel coding which presents a great challenge at the expense of high decoding complexity, power consumption, error floor phenomena, while experiencing performance degradation for short block lengths. For this reason, such a design considerably increases the overall system complexity, which is difficult to implement. Therefore, the existing LTE turbo codes are not recommended in NB-IoT systems and, hence, new channel coding algorithms need to be employed for LPWA specifications. First, LTE-based turbo decoding and frequency-domain turbo equalization algorithms are proposed, modifying the simplified maximum a posteriori probability (MAP) decoder and minimum mean square error (MMSE) Turbo equalization algorithms were appended to different Narrowband Physical Uplink Shared Channel (NPUSCH) subcarriers for interference cancellation. These proposed methods aim to minimize the complexity of realizing the traditional MAP turbo decoder and MMSE estimators in the newly NB-IoT PHY layer features. We compare the system performance in terms of block error rate (BLER) and computational complexity.
Detecting Facial Region and Landmarks at Once via Deep Network
Authors: Kim Taehyung , Mok Jiwon , Lee Euichul
Published: August 2021

For accurate and fast detection of facial landmarks, we propose a new facial landmark detection method. Previous facial landmark detection models generally perform a face detection step before landmark detection. This greatly affects landmark detection performance depending on which face detection model is used. Therefore, we propose a model that can simultaneously detect a face region and a landmark without performing the face detection step before landmark detection. The proposed single-shot detection model is based on the framework of YOLOv3, a one-stage object detection method, and the loss function and structure are altered to learn faces and landmarks at the same time. In addition, EfficientNet-B0 was utilized as the backbone network to increase processing speed and accuracy. The learned database used 300W-LP with 64 facial landmarks. The average normalized error of the proposed model was 2.32 pixels. The processing time per frame was about 15 milliseconds, and the average precision of face detection was about 99%. As a result of the evaluation, it was confirmed that the single-shot detection model has better performance and speed than the previous methods. In addition, as a result of using the COFW database, which has 29 landmarks instead of 64 to verify the proposed method, the average normalization error was 2.56 pixels, which was also confirmed to show promising performance.
Adaptive Content Precaching Scheme Based on the Predictive Speed of Vehicles in Content-Centric Vehicular Networks
Authors: Nam Youngju , Choi Hyunseok , Shin Yongje , Lee Euisin , Lee Eun-Kyu
Published: August 2021

Content-Centric Vehicular Networks (CCVNs) are considered as an attractive technology to efficiently distribute and share contents among vehicles in vehicular environments. Due to the large size of contents such as multimedia data, it might be difficult for a vehicle to download the whole of a content within the coverage of its current RoadSide Unit (RSU). To address this issue, many studies exploit mobility-based content precaching in the next RSU on the trajectory of the vehicle. To calculate the amount of the content precaching, they use a constant speed such as the current speed of the vehicle requesting the content or the average speed of vehicles in the next RSU. However, since they do not appropriately reflect the practical speed of the vehicle in the next RSU, they could incorrectly calculate the amount of the content precaching. Therefore, we propose an adaptive content precaching scheme (ACPS) that correctly estimates the predictive speed of a requester vehicle to reflect its practical speed and calculates the amount of the content precaching using its predictive speed. ACPS adjusts the predictive speed to the average speed starting from the current speed with the optimized adaptive value. To compensate for a subtle error between the predictive and the practical speeds, ACPS appropriately adds a guardband area to the precaching amount. Simulation results verify that ACPS achieves better performance than previous schemes with the current or the average speeds in terms of the content download delay and the backhaul traffic overhead.
Modern Slavery Characterisation through the Analysis of Energy Replenishment
Authors: Garreto Gairo , Baptista João Santos , Mota Antônia , Vaz Mário
Published: August 2021

The Brazilian economy was, until the end of the 19th Century, based on slave labour. However, in this first quarter of the 21st Century, the problem persists. These situations tend to be mistaken with “simple” violations of labour laws. This work aims to establish Occupational Health and Safety parameters, focusing on energy needs, to distinguish between the breach of labour legislation and modern rural slavery in the 21st Century in Brazil. In response to this challenge, bibliographical research was carried out on the feeding and energy replenishment conditions of Brazilian slaves in the 19th Century. Obtained data were compared with a sample where 392 cases of neo-slavery in Brazil are described. The energy spent and the energy supplied was calculated to identify the enslaved workers’ general feeding conditions in the two historical periods. The general conditions of food and water supply were analysed. It was possible to identify three comparable parameters: food quality, food quantity, and water supply. It was concluded that there is a parallelism of energy replenishment conditions between Brazilian slaves and neo-slaves of the 19th and 21st centuries, respectively, different from that of free workers. This difference can help authorities identify and punish instances of modern slavery.