bluuebunny/crossref_metadata_2025 huggingface dataset:
Current Knowledge and Perspectives of Pyrrolizidine Alkaloids in Pharmacological Applications: A Mini-Review
Authors: Wei Xianqin , Ruan Weibin , Vrieling Klaas
Published: March 2021

Pyrrolizidine alkaloids (PAs) are a widespread group of secondary metabolites in plants. PAs are notorious for their acute hepatotoxicity, genotoxicity and neurological damage to humans and animals. In recent decades, the application of PAs for beneficial biological activities to cure disease has drawn greater attention. Here, we review the current knowledge regarding the pharmacological properties of PAs and discuss PAs as promising prototypes for the development of new drugs.
Pea Protein Nanoemulsion Effectively Stabilizes Vitamin D in Food Products: A Potential Supplementation during the COVID-19 Pandemic
Authors: Akkam Yazan , Rababah Taha , Costa Rui , Almajwal Ali , Feng Hao , Laborde Juan E. Andrade , Abulmeaty Mahmoud M. , Razak Suhail
Published: March 2021

Vitamin D deficiency is a global issue which has been exacerbated by the COVID-19 pandemic-related lockdowns. Fortification of food staples with vitamin D provides a solution to alleviate this problem. This research explored the use of pea protein nanoemulsion (PPN) to improve the stability of vitamin D in various food products. PPN was created using a pH-shifting and ultrasonication combined method. The physicochemical properties were studied, including particle size, foaming ability, water holding capacity, antioxidant activity, and total phenolic contents. The fortification of several food formulations (non-fat cow milk, canned orange juice, orange juice powder, banana milk, and infant formula) with vitamin D–PPN was investigated and compared to raw untreated pea protein (UPP) regarding their color, viscosity, moisture content, chemical composition, vitamin D stability, antioxidant activity, and morphology. Finally, a sensory evaluation (quantitative descriptive analysis, and consumer testing) was conducted. The results show that PPN with a size of 21.8 nm protected the vitamin D in all tested products. PPN may serve as a potential carrier and stabilizer of vitamin D in food products with minimum effects on the taste and color. Hence, PPN may serve as a green and safe method for food fortification during the COVID-19 pandemic.
Hard and Soft Protein Corona of Nanomaterials: Analysis and Relevance
Authors: García-Álvarez Rafaela , Vallet-Regí María
Published: March 2021

Upon contact with a biological milieu, nanomaterials tend to interact with biomolecules present in the media, especially proteins, leading to the formation of the so-called “protein corona”. As a result of these nanomaterial–protein interactions, the bio-identity of the nanomaterial is altered, which is translated into modifications of its behavior, fate, and pharmacological profile. For biomedical applications, it is fundamental to understand the biological behavior of nanomaterials prior to any clinical translation. For these reasons, during the last decade, numerous publications have been focused on the investigation of the protein corona of many different types of nanomaterials. Interestingly, it has been demonstrated that the structure of the protein corona can be divided into hard and soft corona, depending on the affinity of the proteins for the nanoparticle surface. In the present document, we explore the differences between these two protein coronas, review the analysis techniques used for their assessment, and reflect on their relevance for medical purposes.
Anisotropic Growth and Magnetic Properties of α″-Fe16N2@C Nanocones
Authors: Li Yong , Kuang Qifeng , Men Xiaoling , Wang Shenggang , Li Da , Choi Chuljin , Zhang Zhidong
Published: March 2021

α″-Fe16N2 nanomaterials with a shape anisotropy for high coercivity performance are of interest in potential applications such as rare-earth-free permanent magnets, which are difficult to synthesize in situ anisotropic growth. Here, we develop a new and facile one-pot microemulsion method with Fe(CO)5 as the iron source and tetraethylenepentamine (TEPA) as the N/C source at low synthesis temperatures to fabricate carbon-coated tetragonal α″-Fe16N2 nanocones. Magnetocrystalline anisotropy energy is suggested as the driving force for the anisotropic growth of α″-Fe16N2@C nanocones because the easy magnetization direction of tetragonal α″-Fe16N2 nanocrystals is along the c axis. The α″-Fe16N2@C nanocones agglomerate to form a fan-like microstructure, in which the thin ends of nanocones direct to its center, due to the magnetostatic energy. The lengths of α″-Fe16N2@C nanocones are ~200 nm and the diameters vary from ~10 nm on one end to ~40 nm on the other end. Carbon shells with a thickness of 2–3 nm protect α″-Fe16N2 nanocones from oxidation in air atmosphere. The α″-Fe16N2@C nanocones synthesized at 433 K show a room-temperature saturation magnetization of 82.6 emu/g and a coercive force of 320 Oe.
Activated Graphene Deposited on Porous Cu Mesh for Supercapacitors
Authors: Lim TaeGyeong , Kim TaeYoung , Suk Ji Won
Published: March 2021

A porous Cu (P-Cu) mesh was used as a current collector and its morphological effect on the supercapacitor performance was investigated. A porous surface was obtained by thermally annealing the Cu mesh using ammonia gas. Hierarchically porous activated graphene (AG) with a high specific surface area (SSA) was deposited on the P-Cu mesh using electrophoretic deposition, aided by graphene oxide (GO). GO was thermally converted to electrically conductive reduced graphene oxide (rGO). The AG/rGO that was deposited on the P-Cu mesh achieved a high specific capacitance of up to 140.0 F/g and a high energy density of up to 3.11 Wh/kg at a current density of 2 A/g in 6 m KOH aqueous electrolyte. The high SSA of AG and the porous surface morphology of the Cu mesh allowed efficient electric double-layer formation and charge transport. This work offers an alternative to improve supercapacitors by combining a porous metallic current collector with porous AG.
Magnetism and ε-τ Phase Transformation in MnAl-Based Nanocomposite Magnets
Authors: Crisan Alina Daniela , Leca Aurel , Bartha Cristina , Dan Ioan , Crisan Ovidiu
Published: March 2021

Melt spun ribbons of Mn53Al45C2 and Mn52Al46C2 have been synthesized by rapid quenching of the melt with the purpose of monitoring the ε-τ phase transformation to show technologically feasible ways to increase magnetic parameters and to illustrate the viability of these alloys as the next generation of rare earth (RE)-free magnets. By differential scanning calorimetry (DSC), activation energies and temperatures of onset of the ε-τ phase transformation were obtained. Structural analysis was performed using X-ray diffraction (XRD) and the resulting XRD patterns were quantitatively assessed using full profile Rietveld-type analysis. Appropriate annealing was performed in order to enable the ε-τ phase transformation. While hcp ε-phase was found to be predominant in the as-cast samples, after appropriate annealing, the tetragonal τ-phase, the one that furnishes the relevant magnetic response, was found to be predominant with an abundance of about 90%. The data suggested a mechanism of hcp ε-phase decomposition controlled by the segregation towards the interfacial regions, having the rate of transformation governed by antiphase boundary diffusion processes. Magnetic measurements of annealed sample Mn53Al45C2, consisting of predominant tetragonal τ-phase, showed high values of magnetization and increased coercivity, consistent with an energy product of about 10 MGOe, similar with previously reported magnetization measurements, providing further insight into the realization of future class of RE-free low-cost permanent magnets.
Dietary Habit Is Associated with Depression and Intelligence: An Observational and Genome-Wide Environmental Interaction Analysis in the UK Biobank Cohort
Authors: Cheng Bolun , Chu Xiaomeng , Yang Xuena , Wen Yan , Jia Yumeng , Liang Chujun , Yao Yao , Ye Jing , Cheng Shiqiang , Liu Li , Wu Cuiyan , Zhang Feng
Published: March 2021

Dietary habits have considerable impact on brain development and mental health. Despite long-standing interest in the association of dietary habits with mental health, few population-based studies of dietary habits have assessed depression and fluid intelligence. Our aim is to investigate the association of dietary habits with depression and fluid intelligence. In total, 814 independent loci were utilized to calculate the individual polygenic risk score (PRS) for 143 dietary habit-related traits. The individual genotype data were obtained from the UK Biobank cohort. Regression analyses were then conducted to evaluate the association of dietary habits with depression and fluid intelligence, respectively. PLINK 2.0 was utilized to detect the single nucleotide polymorphism (SNP) × dietary habit interaction effect on the risks of depression and fluid intelligence. We detected 22 common dietary habit-related traits shared by depression and fluid intelligence, such as red wine glasses per month, and overall alcohol intake. For interaction analysis, we detected that OLFM1 interacted with champagne/white wine in depression, while SYNPO2 interacted with coffee type in fluid intelligence. Our study results provide novel useful information for understanding how eating habits affect the fluid intelligence and depression.
Association of Dietary Patterns with Weight Status and Metabolic Risk Factors among Children and Adolescents
Authors: Oh Seulki , Lee So Yeong , Kim Do-Yeon , Woo Sarah , Kim YoonMyung , Lee Hye-Ja , Jang Han Byul , Park Sang Ick , Park Kyung Hee , Lim Hyunjung
Published: March 2021

Unhealthy dietary patterns are associated with obesity in children and adolescents. However, few studies have investigated the relationships between dietary patterns and obesity-related metabolic disorders among Asians. We identified dietary patterns in children and adolescents and examined the associations between these patterns and obesity, insulin resistance, and metabolic syndrome in South Korea. This study is a cross-sectional design. We used baseline data from an intervention study of 435 Korean children and adolescents aged 6–17 years. Insulin resistance was assessed as HOMA-IR ≥ 2.6. Metabolic syndrome was diagnosed by cardiovascular disease risk factor clustering. Dietary intakes were estimated using 3-day food records. Factor analysis was used to obtain dietary patterns, and we examined the associations between dietary patterns and obesity-related markers adjusted for potential covariates. Three dietary patterns were identified as fast food and soda (FFS), white rice and kimchi (WRK), and oil and seasoned vegetable (OSV) patterns. Compared with participants in the lower intake of FFS pattern, those in the top intake were associated with a higher waist circumference (WC) (β = 1.55), insulin level (β = 1.25), and body mass index (BMI) (β = 0.53) and it was positively associated with HOMA-IR ≥ 2.6 (OR = 2.11; 95% CI: 1.227–3.638) (p < 0.05). WRK pattern was associated with lower weight and higher HDL cholesterol, and the OSV pattern was associated with a lower weight, WC, and insulin level (p < 0.05). The FFS pattern showed a positive relation with WC, serum insulin, and BMI, and the other two dietary patterns indicated a preventive effect of those parameters. The FFS pattern was associated with significantly elevated insulin resistance among children and adolescents.
Feasibility of Vitamin C in the Treatment of Post Viral Fatigue with Focus on Long COVID, Based on a Systematic Review of IV Vitamin C on Fatigue
Authors: Vollbracht Claudia , Kraft Karin
Published: March 2021

Fatigue is common not only in cancer patients but also after viral and other infections. Effective treatment options are still very rare. Therefore, the present knowledge on the pathophysiology of fatigue and the potential positive impact of treatment with vitamin C is illustrated. Additionally, the effectiveness of high-dose IV vitamin C in fatigue resulting from various diseases was assessed by a systematic literature review in order to assess the feasibility of vitamin C in post-viral, especially in long COVID, fatigue. Nine clinical studies with 720 participants were identified. Three of the four controlled trials observed a significant decrease in fatigue scores in the vitamin C group compared to the control group. Four of the five observational or before-and-after studies observed a significant reduction in pre–post levels of fatigue. Attendant symptoms of fatigue such as sleep disturbances, lack of concentration, depression, and pain were also frequently alleviated. Oxidative stress, inflammation, and circulatory disorders, which are important contributors to fatigue, are also discussed in long COVID fatigue. Thus, the antioxidant, anti-inflammatory, endothelial-restoring, and immunomodulatory effects of high-dose IV vitamin C might be a suitable treatment option.
Antenatal Determinants of Childhood Obesity in High-Risk Offspring: Protocol for the DiGest Follow-Up Study
Authors: Jones Danielle , De Lucia Rolfe Emanuella , Rennie Kirsten L. , Griep Linda M. Oude , Kusinski Laura C. , Hughes Deborah J. , Brage Soren , Ong Ken K. , Beardsall Kathryn , Meek Claire L.
Published: March 2021

Childhood obesity is an area of intense concern internationally and is influenced by events during antenatal and postnatal life. Although pregnancy complications, such as gestational diabetes and large-for-gestational-age birthweight have been associated with increased obesity risk in offspring, very few successful interventions in pregnancy have been identified. We describe a study protocol to identify if a reduced calorie diet in pregnancy can reduce adiposity in children to 3 years of age. The dietary intervention in gestational diabetes (DiGest) study is a randomised, controlled trial of a reduced calorie diet provided by a whole-diet replacement in pregnant women with gestational diabetes. Women receive a weekly dietbox intervention from enrolment until delivery and are blinded to calorie allocation. This follow-up study will assess associations between a reduced calorie diet in pregnancy with offspring adiposity and maternal weight and glycaemia. Anthropometry will be performed in infants and mothers at 3 months, 1, 2 and 3 years post-birth. Glycaemia will be assessed using bloodspot C-peptide in infants and continuous glucose monitoring with HbA1c in mothers. Data regarding maternal glycaemia in pregnancy, maternal nutrition, infant birthweight, offspring feeding behaviour and milk composition will also be collected. The DiGest follow-up study is expected to take 5 years, with recruitment finishing in 2026.
Characterization of Ocular Surface Microbial Profiles Revealed Discrepancies between Conjunctival and Corneal Microbiota
Authors: Matysiak Anna , Kabza Michal , Karolak Justyna A. , Jaworska Marcelina M. , Rydzanicz Malgorzata , Ploski Rafal , Szaflik Jacek P. , Gajecka Marzena
Published: March 2021

The ocular microbiome composition has only been partially characterized. Here, we used RNA-sequencing (RNA-Seq) data to assess microbial diversity in human corneal tissue. Additionally, conjunctival swab samples were examined to characterize ocular surface microbiota. Short RNA-Seq reads, obtained from a previous transcriptome study of 50 corneal tissues, were mapped to the human reference genome GRCh38 to remove sequences of human origin. The unmapped reads were then used for taxonomic classification by comparing them with known bacterial, archaeal, and viral sequences from public databases. The components of microbial communities were identified and characterized using both conventional microbiology and polymerase chain reaction (PCR) techniques in 36 conjunctival swabs. The majority of ocular samples examined by conventional and molecular techniques showed very similar microbial taxonomic profiles, with most of the microorganisms being classified into Proteobacteria, Firmicutes, and Actinobacteria phyla. Only 50% of conjunctival samples exhibited bacterial growth. The PCR detection provided a broader overview of positive results for conjunctival materials. The RNA-Seq assessment revealed significant variability of the corneal microbial communities, including fastidious bacteria and viruses. The use of the combined techniques allowed for a comprehensive characterization of the eye microbiome’s elements, especially in aspects of microbiota diversity.
Production of Reproducible Filament Batches for the Fabrication of 3D Printed Oral Forms
Authors: Roulon Stéphane , Soulairol Ian , Lavastre Valérie , Payre Nicolas , Cazes Maxime , Delbreilh Laurent , Alié Jean
Published: March 2021

Patients need medications at a dosage suited to their physiological characteristics. Three-dimensional printing (3DP) technology by fused-filament fabrication (FFF) is a solution for manufacturing medication on demand. The aim of this work was to identify important parameters for the production of reproducible filament batches used by 3DP for oral formulations. Amiodarone hydrochloride, an antiarrhythmic and insoluble drug, was chosen as a model drug because of dosage adaptation need in children. Polyethylene oxide (PEO) filaments containing amiodarone hydrochloride were produced by hot-melt extrusion (HME). Different formulation storage conditions were investigated. For all formulations, the physical form of the drug following HME and fused-deposition modeling (FDM) 3D-printing processes were assessed using thermal analysis and X-ray powder diffraction (XRPD). Filament mechanical properties, linear mass density and surface roughness, were investigated by, respectively, 3-point bending, weighing, and scanning electron microscopy (SEM). Analysis results showed that the formulation storage condition before HME-modified filament linear mass density and, therefore, the oral forms masses from a batch to another. To obtain constant filament apparent density, it has been shown that a constant and reproducible drying condition is required to produce oral forms with constant mass.
A Comparative Review of Natural and Synthetic Biopolymer Composite Scaffolds
Authors: Reddy M. Sai Bhargava , Ponnamma Deepalekshmi , Choudhary Rajan , Sadasivuni Kishor Kumar
Published: March 2021

Tissue engineering (TE) and regenerative medicine integrate information and technology from various fields to restore/replace tissues and damaged organs for medical treatments. To achieve this, scaffolds act as delivery vectors or as cellular systems for drugs and cells; thereby, cellular material is able to colonize host cells sufficiently to meet up the requirements of regeneration and repair. This process is multi-stage and requires the development of various components to create the desired neo-tissue or organ. In several current TE strategies, biomaterials are essential components. While several polymers are established for their use as biomaterials, careful consideration of the cellular environment and interactions needed is required in selecting a polymer for a given application. Depending on this, scaffold materials can be of natural or synthetic origin, degradable or nondegradable. In this review, an overview of various natural and synthetic polymers and their possible composite scaffolds with their physicochemical properties including biocompatibility, biodegradability, morphology, mechanical strength, pore size, and porosity are discussed. The scaffolds fabrication techniques and a few commercially available biopolymers are also tabulated.
Additive Manufacturing of Wood Flour/PHA Composites Using Micro-Screw Extrusion: Effect of Device and Process Parameters on Performance
Authors: Tian Jing , Zhang Run , Yang Jiayuan , Chou Weimin , Xue Ping , Ding Yun
Published: March 2021

Based on additive manufacturing of wood flour and polyhydroxyalkanoates composites using micro-screw extrusion, device and process parameters were evaluated to achieve a reliable printing. The results show that the anisotropy of samples printed by micro-screw extrusion is less obvious than that of filament extrusion fused deposition modeling. The type of micro-screw, printing speed, layer thickness, and nozzle diameter have significant effects on the performance of printed samples. The linear relationship between the influencing parameters and the screw speed is established, therefore, the performance of printed products can be controlled by the extrusion flow rate related to screw speed.
Be Careful, Ye Catholic: The Entanglement of Mormonism and Money in Peru
Authors: Palmer Jason
Published: March 2021

Globalization is not only the feverish insistence that land’s superfluity is increasing exponentially, but it is also the willful ignorance of the reality underlying that illusion: Distance has not been annihilated. Distance, and the land it spans, is more important than ever. Globalization imagines away the land’s importance because of whom it imagines to be “of the land”. This entity, indigeneity, threatens to expose the lie upon which globalization is founded. According to many people of the land surrounding the mid-Andean city of Arequipa, Peru, globalization’s promise of unidirectional wealth accumulation severs their connection to sustainable, terrestrial cyclicity. For some of these arequipeños, few institutions embody this existential disruption more menacingly than The Church of Jesus Christ of Latter-Day Saints. Mormonism, therefore, becomes a material and mythological threat to the lifeways of their land. This article grounds the fraught, mimetic relationship between globalization and land in Peru through the lens of anti-Mormonism.
Not Mining Sterilization of Explored Mineral Resources. The Example of Native Sulfur Deposits in Poland Case History
Authors: Sermet Edyta , Nieć Marek
Published: March 2021

The sterilization of mineral resources makes considerable amounts inaccessible for future use and may be a barrier to the free supply of commodities. During the exploitation of mineral deposits, some parts of their resources become sterilized as inaccessible because of natural hazards or unfavorable economic conditions. Not mining land use and the social opposition against mining is the purpose of sterilization of considerable demonstrated mineral resources of deposits not yet engaged in exploitation. The native sulfur deposits in Poland are a good example of such “not mining” sterilization, which makes a considerable part of known resources inaccessible. On the northern border of the Carpathian Foredeep within the Miocene gypsum formation, the systematic exploration had demonstrated about 1 billion tons of sulfur resources located in the deposits of varied dimensions. The sulfur opencast mining and underground melting (the modified Frasch method) flourished from 1958 up to 1993. The increasing sulfur supply, recoverable from hydrocarbons, caused the closing down of sulfur mines, leaving a place with considerable untouched resources. About 67% of sulfur resources left by closed mines and of other explored but not exploited deposits are sterilized by the advancement of settlements, industrial plants, road construction, and by social opposition against mining.
Particle Size-Frequency Distributions of the OSIRIS-REx Candidate Sample Sites on Asteroid (101955) Bennu
Authors: Burke Keara N. , DellaGiustina Daniella N. , Bennett Carina A. , Walsh Kevin J. , Pajola Maurizio , Bierhaus Edward B. , Nolan Michael C. , Boynton William V. , Brodbeck Juliette I. , Connolly Harold C. , Prasanna Deshapriya Jasinghege Don , Dworkin Jason P. , Elder Catherine M. , Golish Dathon R. , Hoover Rachael H. , Jawin Erica R. , McCoy Timothy J. , Michel Patrick , Molaro Jamie L. , Nolau Jennifer O. , Padilla Jacob , Rizk Bashar , Robbins Stuart J. , Sahr Eric M. , Smith Peter H. , Stewart Stephanie J. , Susorney Hannah C. M. , Enos Heather L. , Lauretta Dante S.
Published: March 2021

We manually mapped particles ranging in longest axis from 0.3 cm to 95 m on (101955) Bennu for the Origins, Spectral Interpretation, Resource Identification, and Security–Regolith Explorer (OSIRIS-REx) asteroid sample return mission. This enabled the mission to identify candidate sample collection sites and shed light on the processes that have shaped the surface of this rubble-pile asteroid. Building on a global survey of particles, we used higher-resolution data from regional observations to calculate particle size-frequency distributions (PSFDs) and assess the viability of four candidate sites for sample collection (presence of unobstructed particles ≤ 2 cm). The four candidate sites have common characteristics: each is situated within a crater with a relative abundance of sampleable material. Their PSFDs, however, indicate that each site has experienced different geologic processing. The PSFD power-law slopes range from −3.0 ± 0.2 to −2.3 ± 0.1 across the four sites, based on images with a 0.01-m pixel scale. These values are consistent with, or shallower than, the global survey measurements. At one site, Osprey, the particle packing density appears to reach geometric saturation. We evaluate the uncertainty in these measurements and discuss their implications for other remotely sensed and mapped particles, and their importance to OSIRIS-REx sampling operations.
Autonomous Lidar-Based Monitoring of Coastal Lagoon Entrances
Authors: Arshad Bilal , Barthelemy Johan , Perez Pascal
Published: March 2021

Intermittently closed and open lakes or Lagoons (ICOLLs) are characterised by entrance barriers that form or break down due to the action of wind, waves and currents until the ocean-lagoon exchange becomes discontinuous. Entrance closure raises a variety of management issues that are regulated by monitoring. In this paper, those issues are investigated, and an automated sensor solution is proposed. Based upon a static Lidar paired with an edge computing device. This solar-powered remote sensing device provides an efficient way to automatically survey the lagoon entrance and estimate the berm profile. Additionally, it estimates the dry notch location and its height, critical factors in the management of the lagoon entrances. Generated data provide valuable insights into landscape evolution and berm behaviour during natural and mechanical breach events.
Deep Neural Networks for Quantitative Damage Evaluation of Building Losses Using Aerial Oblique Images: Case Study on the Great Wall (China)
Authors: Gong Yiping , Zhang Fan , Jia Xiangyang , Huang Xianfeng , Li Deren , Mao Zhu
Published: March 2021

Automated damage evaluation is of great importance in the maintenance and preservation of heritage structures. Damage investigation of large cultural buildings is time-consuming and labor-intensive, meaning that many buildings are not repaired in a timely manner. Additionally, some buildings in harsh environments are impossible to reach, increasing the difficulty of damage investigation. Oblique images facilitate damage detection in large buildings, yet quantitative damage information, such as area or volume, is difficult to generate. In this paper, we propose a method for quantitative damage evaluation of large heritage buildings in wild areas with repetitive structures based on drone images. Unlike existing methods that focus on building surfaces, we study the damage of building components and extract hidden linear symmetry information, which is useful for localizing missing parts in architectural restoration. First, we reconstruct a 3D mesh model based on the photogrammetric method using high-resolution oblique images captured by drone. Second, we extract 3D objects by applying advanced deep learning methods to the images and projecting the 2D object segmentation results to 3D mesh models. For accurate 2D object extraction, we propose an edge-enhanced method to improve the segmentation accuracy of object edges. 3D object fragments from multiple views are integrated to build complete individual objects according to the geometric features. Third, the damage condition of objects is estimated in 3D space by calculating the volume reduction. To obtain the damage condition of an entire building, we define the damage degree in three levels: no or slight damage, moderate damage and severe damage, and then collect statistics on the number of damaged objects at each level. Finally, through an analysis of the building structure, we extract the linear symmetry surface from the remaining damaged objects and use the symmetry surface to localize the positions of missing objects. This procedure was tested and validated in a case study (the Jiankou Great Wall in China). The experimental results show that in terms of segmentation accuracy, our method obtains results of 93.23% mAP and 84.21% mIoU on oblique images and 72.45% mIoU on the 3D mesh model. Moreover, the proposed method shows effectiveness in performing damage assessment of objects and missing part localization.
Intra-Urban Scaling Properties Examined by Automatically Extracted City Hotspots from Street Data and Nighttime Light Imagery
Authors: Ma Ding , Guo Renzhong , Jing Ying , Zheng Ye , Zhao Zhigang , Yang Jiahao
Published: March 2021

A country can be well-comprehended through its core cities. Similarly, we can learn about a city from its hotspots, as they manifest the concentration of urban infrastructures and human activities. Following this philosophy, this paper studies the intra-urban form and function from a complexity science perspective by exploring the power law distribution of hotspot sizes and related socio-economic attributes. To detect hotspots, we rely on spatial clustering of geospatial big data sets, including street data from OpenStreetMap platform and nighttime light (NTL) data from the visible infrared imaging radiometer suite (VIIRS) imagery. Unlike conventional spatial units, which are imposed by governments or authorities (such as census block), the delineation of hotspots is done in a totally bottom-up manner and, more importantly, can help us examine precisely the scaling pattern of urban morphological and functional aspects. This results in two types of urban hotspots—street-based and NTL-based hotspots—being generated across 20 major cities in China. We find that Zipf’s law of hotspot sizes (both types) holds remarkably well for each city, as do the city-size distributions at the country level, indicating a statistically self-similar structure of geographic space. We further find that the urban scaling law can be effectively detected when using NTL-based hotspots as basic units. Furthermore, the comparison between two types of hotspots enables us to gain in-depth insights of urban planning and urban economic development.
Feature-Level Fusion of Polarized SAR and Optical Images Based on Random Forest and Conditional Random Fields
Authors: Kong Yingying , Yan Biyuan , Liu Yanjuan , Leung Henry , Peng Xiangyang
Published: March 2021

In terms of land cover classification, optical images have been proven to have good classification performance. Synthetic Aperture Radar (SAR) has the characteristics of working all-time and all-weather. It has more significant advantages over optical images for the recognition of some scenes, such as water bodies. One of the current challenges is how to fuse the benefits of both to obtain more powerful classification capabilities. This study proposes a classification model based on random forest with the conditional random fields (CRF) for feature-level fusion classification using features extracted from polarized SAR and optical images. In this paper, feature importance is introduced as a weight in the pairwise potential function of the CRF to improve the correction rate of misclassified points. The results show that the dataset combining the two provides significant improvements in feature identification when compared to the dataset using optical or polarized SAR image features alone. Among the four classification models used, the random forest-importance_ conditional random fields (RF-Im_CRF) model developed in this paper obtained the best overall accuracy (OA) and Kappa coefficient, validating the effectiveness of the method.
A Case Study of Polar Cap Sporadic-E Layer Associated with TEC Variations
Authors: Wang Yong , Jayachandran Periyadan T. , Themens David R. , McCaffrey Anthony M. , Zhang Qing-He , David Shiva , Chadwick Richard
Published: March 2021

The Sporadic-E (Es) layer is an often-observed phenomenon at high latitudes; however, our understanding of the polar cap Es layer is severely limited due to the scarce number of measurements. Here, the first comprehensive study of the polar cap Es layer associated with Global Positioning System (GPS) Total Electron Content (TEC) variations and scintillations is presented with multiple measurements at Resolute, Canada (Canadian Advanced Digital Ionosonde (CADI), Northward-looking face of Resolute Incoherent-Scatter Radar (RISR-N), and GPS receiver). According to the joint observations, the polar cap Es layer is a thin patch structure with variously high electron density, which gradually develops into the lower E region (~100 km) and horizontally extends >200 km. Moreover, the TEC variations produced by the polar cap Es layer are pulse-like enhancements with a general amplitude of ~0.5 TECu and are followed by smaller but rapid TEC perturbations. Furthermore, the possible scintillation effects likely associated with the polar cap Es layer are also discussed. As a consequence, the results widely expand our understanding on the polar cap Es layer, in particular on TEC variations.
Mesoscale Temporal Wind Variability Biases Global Air–Sea Gas Transfer Velocity of CO2 and Other Slightly Soluble Gases
Authors: Gu Yuanyuan , Katul Gabriel G. , Cassar Nicolas
Published: March 2021

The significance of the water-side gas transfer velocity for air–sea CO2 gas exchange (k) and its non-linear dependence on wind speed (U) is well accepted. What remains a subject of inquiry are biases associated with the form of the non-linear relation linking k to U (hereafter labeled as f(U), where f(.) stands for an arbitrary function of U), the distributional properties of U (treated as a random variable) along with other external factors influencing k, and the time-averaging period used to determine k from U. To address the latter issue, a Taylor series expansion is applied to separate f(U) into a term derived from time-averaging wind speed (labeled as ⟨U⟩, where ⟨.⟩ indicates averaging over a monthly time scale) as currently employed in climate models and additive bias corrections that vary with the statistics of U. The method was explored for nine widely used f(U) parameterizations based on remotely-sensed 6-hourly global wind products at 10 m above the sea-surface. The bias in k of monthly estimates compared to the reference 6-hourly product was shown to be mainly associated with wind variability captured by the standard deviation σσU around ⟨U⟩ or, more preferably, a dimensionless coefficient of variation Iu= σσU/⟨U⟩. The proposed correction outperforms previous methodologies that adjusted k when using ⟨U⟩ only. An unexpected outcome was that upon setting Iu2 = 0.15 to correct biases when using monthly wind speed averages, the new model produced superior results at the global and regional scale compared to prior correction methodologies. Finally, an equation relating Iu2 to the time-averaging interval (spanning from 6 h to a month) is presented to enable other sub-monthly averaging periods to be used. While the focus here is on CO2, the theoretical tactic employed can be applied to other slightly soluble gases. As monthly and climatological wind data are often used in climate models for gas transfer estimates, the proposed approach provides a robust scheme that can be readily implemented in current climate models.
Automated Mapping of Ms 7.0 Jiuzhaigou Earthquake (China) Post-Disaster Landslides Based on High-Resolution UAV Imagery
Authors: Liang Rubing , Dai Keren , Shi Xianlin , Guo Bin , Dong Xiujun , Liang Feng , Tomás Roberto , Wen Ningling , Fan Xuanmei
Published: March 2021

The Ms 7.0 Jiuzhaigou earthquake that occurred on 8 August 2017 triggered hundreds of landslides in the Jiuzhaigou valley scenic and historic-interest area in Sichuan, China, causing heavy casualties and serious property losses. Quick and accurate mapping of post-disaster landslide distribution is of paramount importance for earthquake emergency rescue and the analysis of post-seismic landslides distribution characteristics. The automatic identification of landslides is mostly based on medium- and low-resolution satellite-borne optical remote-sensing imageries, and the high-accuracy interpretation of earthquake-triggered landslides still relies on time-consuming manual interpretation. This paper describes a methodology based on the use of 1 m high-resolution unmanned aerial vehicle (UAV) imagery acquired after the earthquake, and proposes a support vector machine (SVM) classification method combining the roads and villages mask from pre-seismic remote sensing imagery to accurately and automatically map the landslide inventory. Compared with the results of manual visual interpretation, the automatic recognition accuracy could reach 99.89%, and the Kappa coefficient was higher than 0.9, suggesting that the proposed method and 1 m high-resolution UAV imagery greatly improved the mapping accuracy of the landslide area. We also analyzed the spatial-distribution characteristics of earthquake-triggered landslides with the influenced factors of altitude, slope gradient, slope aspect, and the nearest faults, which provided important support for the further study of post-disaster landslide distribution characteristics, susceptibility prediction, and risk assessment.
vis–NIR and XRF Data Fusion and Feature Selection to Estimate Potentially Toxic Elements in Soil
Authors: Gholizadeh Asa , Coblinski João A. , Saberioon Mohammadmehdi , Ben-Dor Eyal , Drábek Ondřej , Demattê José A. M. , Borůvka Luboš , Němeček Karel , Chabrillat Sabine , Dajčl Julie
Published: March 2021

Soil contamination by potentially toxic elements (PTEs) is intensifying under increasing industrialization. Thus, the ability to efficiently delineate contaminated sites is crucial. Visible–near infrared (vis–NIR: 350–2500 nm) and X-ray fluorescence (XRF: 0.02–41.08 keV) spectroscopic techniques have attracted tremendous attention for the assessment of PTEs. Recently, the application of fused vis–NIR and XRF spectroscopy, which is based on the complementary effect of data fusion, is also increasing. Moreover, different data manipulation methods, including feature selection approaches, affect the prediction performance. This study investigated the feasibility of using single and fused vis–NIR and XRF spectra while exploring feature selection algorithms for the assessment of key soil PTEs. The soil samples were collected from one of the most heavily polluted areas of the Czech Republic and scanned using laboratory vis–NIR and XRF spectrometers. Univariate filter (UF) and genetic algorithm (GA) were used to select the bands of greater importance for the PTE prediction. Support vector machine (SVM) was then used to train the models using the full-range and feature-selected spectra of single sensors and their fusion. It was found that XRF spectra alone (primarily GA-selected) performed better than single vis–NIR and fused spectral data for predictions of PTEs. Moreover, the prediction models that were derived from the fused data set (particularly the GA-selected) enhanced the models’ accuracies as compared with the single vis–NIR spectra. In general, the results suggest that the GA-selected spectra obtained from the single XRF spectrometer (for As and Pb) and from the fusion of vis–NIR and XRF (for Pb) are promising for accurate quantitative estimation detection of the mentioned PTEs.
Velocity-Free Attitude Control of Quadrotors: A Nonlinear Negative Imaginary Approach
Authors: Ghallab Ahmed G. , Petersen Ian R.
Published: March 2021

In this paper, we propose a new approach to the attitude control of quadrotors, by which angular velocity measurements or a model-based observer reconstructing the angular velocity are not needed. The proposed approach is based on recent stability results obtained for nonlinear negative imaginary systems. In specific, through an inner-outer loop method, we establish the nonlinear negative imaginary property of the quadrotor rotational subsystem. Then, a strictly negative imaginary controller is synthesized using the nonlinear negative imaginary results. This guarantees the robust asymptotic stability of the attitude of the quadrotor in the face of modeling uncertainties and external disturbances. First simulation results underline the effectiveness of the proposed attitude control approach are presented.
A Generative Method for Indoor Localization Using Wi-Fi Fingerprinting
Authors: Belmonte-Fernández Óscar , Sansano-Sansano Emilio , Caballer-Miedes Antonio , Montoliu Raúl , García-Vidal Rubén , Gascó-Compte Arturo
Published: March 2021

Indoor localization is an enabling technology for pervasive and mobile computing applications. Although different technologies have been proposed for indoor localization, Wi-Fi fingerprinting is one of the most used techniques due to the pervasiveness of Wi-Fi technology. Most Wi-Fi fingerprinting localization methods presented in the literature are discriminative methods. We present a generative method for indoor localization based on Wi-Fi fingerprinting. The Received Signal Strength Indicator received from a Wireless Access Point is modeled by a hidden Markov model. Unlike other algorithms, the use of a hidden Markov model allows ours to take advantage of the temporal autocorrelation present in the Wi-Fi signal. The algorithm estimates the user’s location based on the hidden Markov model, which models the signal and the forward algorithm to determine the likelihood of a given time series of Received Signal Strength Indicators. The proposed method was compared with four other well-known Machine Learning algorithms through extensive experimentation with data collected in real scenarios. The proposed method obtained competitive results in most scenarios tested and was the best method in 17 of 60 experiments performed.
Characteristics of Onefold Clocks of GPS, Galileo, BeiDou and GLONASS Systems
Authors: Ai Qingsong , Maciuk Kamil , Lewinska Paulina , Borowski Lukasz
Published: March 2021

This research is focused on searching for frequency and noise characteristics for available GNSS (Global Navigation Satellite Systems). The authors illustrated frequency stability and noise characteristics for a selected set of data from four different GNSS systems. For this purpose, 30-s-interval clock corrections were used for the GPS weeks 1982–2034 (the entirety of 2018). Firstly, phase data (raw clock corrections) were preprocessed for shifts and removal of outliers; GLONASS and GPS satellites characterize a smaller number of outliers than BeiDou and Galileo clock products. Secondly, frequency and Hadamard deviation were calculated. This study concludes that the stability of GPS and Galileo is better than that of BDS (BeiDou Navigation Satellite System) and GLONASS. Regarding noise, the GPS, Galileo, and BDS clocks are affected by the random walk modulation noise (RWFM), flashing frequency modulation noise (FFM), and white frequency modulation noise (WFM), whereas the GLONASS clocks are mainly affected only by WFM.
Improving Stress Management and Sleep Hygiene in Intelligent Homes
Authors: Leonidis Asterios , Korozi Maria , Sykianaki Eirini , Tsolakou Eleni , Kouroumalis Vasilios , Ioannidi Danai , Stavridakis Andreas , Antona Margherita , Stephanidis Constantine
Published: March 2021

High stress levels and sleep deprivation may cause several mental or physical health issues, such as depression, impaired memory, decreased motivation, obesity, etc. The COVID-19 pandemic has produced unprecedented changes in our lives, generating significant stress, and worries about health, social isolation, employment, and finances. To this end, nowadays more than ever, it is crucial to deliver solutions that can help people to manage and control their stress, as well as to reduce sleep disturbances, so as to improve their health and overall quality of life. Technology, and in particular Ambient Intelligence Environments, can help towards that direction, when considering that they are able to understand the needs of their users, identify their behavior, learn their preferences, and act and react in their interest. This work presents two systems that have been designed and developed in the context of an Intelligent Home, namely CaLmi and HypnOS, which aim to assist users that struggle with stress and poor sleep quality, respectively. Both of the systems rely on real-time data collected by wearable devices, as well as contextual information retrieved from the ambient facilities of the Intelligent Home, so as to offer appropriate pervasive relaxation programs (CaLmi) or provide personalized insights regarding sleep hygiene (HypnOS) to the residents. This article will describe the design process that was followed, the functionality of both systems, the results of the user studies that were conducted for the evaluation of their end-user applications, and a discussion about future plans.
Molecularly Imprinted Polymer-Based Sensors for Priority Pollutants
Authors: Zarejousheghani Mashaalah , Rahimi Parvaneh , Borsdorf Helko , Zimmermann Stefan , Joseph Yvonne
Published: March 2021

Globally, there is growing concern about the health risks of water and air pollution. The U.S. Environmental Protection Agency (EPA) has developed a list of priority pollutants containing 129 different chemical compounds. All of these chemicals are of significant interest due to their serious health and safety issues. Permanent exposure to some concentrations of these chemicals can cause severe and irrecoverable health effects, which can be easily prevented by their early identification. Molecularly imprinted polymers (MIPs) offer great potential for selective adsorption of chemicals from water and air samples. These selective artificial bio(mimetic) receptors are promising candidates for modification of sensors, especially disposable sensors, due to their low-cost, long-term stability, ease of engineering, simplicity of production and their applicability for a wide range of targets. Herein, innovative strategies used to develop MIP-based sensors for EPA priority pollutants will be reviewed.
Decentralized Trusted Data Sharing Management on Internet of Vehicle Edge Computing (IoVEC) Networks Using Consortium Blockchain
Authors: Firdaus Muhammad , Rahmadika Sandi , Rhee Kyung-Hyune
Published: March 2021

The emergence of the Internet of Vehicles (IoV) aims to facilitate the next generation of intelligent transportation system (ITS) applications by combining smart vehicles and the internet to improve traffic safety and efficiency. On the other hand, mobile edge computing (MEC) technology provides enormous storage resources with powerful computing on the edge networks. Hence, the idea of IoV edge computing (IoVEC) networks has grown to be an assuring paradigm with various opportunities to advance massive data storage, data sharing, and computing processing close to vehicles. However, the participant’s vehicle may be unwilling to share their data since the data-sharing system still relies on a centralized server approach with the potential risk of data leakage and privacy security. In addition, vehicles have difficulty evaluating the credibility of the messages they received because of untrusted environments. To address these challenges, we propose consortium blockchain and smart contracts to accomplish a decentralized trusted data sharing management system in IoVEC. This system allows vehicles to validate the credibility of messages from their neighboring by generating a reputation rating. Moreover, the incentive mechanism is utilized to trigger the vehicles to store and share their data honestly; thus, they will obtain certain rewards from the system. Simulation results substantially display an efficient network performance along with forming an appropriate incentive model to reach a decentralized trusted data sharing management of IoVEC networks.