bluuebunny/crossref_metadata_2025 huggingface dataset:
Genome Wide Analysis of Amino Acid Transporter Superfamily in Solanum lycopersicum
Authors: Omari Alzahrani Fatima
Published: February 2021

Amino acid transporters (AATs) are integral membrane proteins and have several functions, including transporting amino acids across cellular membranes. They are critical for plant growth and development. This study comprehensively identified AAT-encoding genes in tomato (Solanum lycopersicum), which is an important vegetable crop and serves as a model for fleshy fruit development. In this study, 88 genes were identified in the S. lycopersicum genome and grouped into 12 subfamilies, based on previously identified AATs in Arabidopsis, rice (Oryza sativa), and potato (Solanum tuberosum) plants. Chromosomal localization revealed that S. lycopersicum AAT (SlAAT) genes are distributed on the 12 S. lycopersicum chromosomes. Segmental duplication events contribute mainly to the expansion of SlAAT genes and about 32% (29 genes) of SlAAT genes were found to originate from this type of event. Expression profiles of SlAAT genes in various tissues of S. lycopersicum using RNA sequencing data from the Tomato Functional Genomics Database showed that SlAAT genes exhibited tissue-specific expression patterns. Comprehensive data generated in this study will provide a platform for further studies on the SlAAT gene family and will facilitate the functional characterization of SlAAT genes.
Genetic and Epigenetic Changes during the Upward Expansion of Deyeuxia angustifolia Kom. in the Alpine Tundra of the Changbai Mountains, China
Authors: Ni Biao , You Jian , Li Jiangnan , Du Yingda , Zhao Wei , Chen Xia
Published: February 2021

Ecological adaptation plays an important role in the process of plant expansion, and genetics and epigenetics are important in the process of plant adaptation. In this study, genetic and epigenetic analyses and soil properties were performed on D. angustifolia of 17 populations, which were selected in the tundra zone on the western slope of the Changbai Mountains. Our results showed that the levels of genetic and epigenetic diversity of D. angustifolia were relatively low, and the main variation occurred among different populations (amplified fragment length polymorphism (AFLP): 95%, methylation sensitive amplification polymorphism (MSAP): 87%). In addition, DNA methylation levels varied from 23.36% to 35.70%. Principal component analysis (PCA) results showed that soil properties of different populations were heterogeneous. Correlation analyses showed that soil moisture, pH and total nitrogen were significantly correlated with genetic diversity of D. angustifolia, and soil temperature and pH were closely related to epigenetic diversity. Simple Mantel tests and partial Mantel tests showed that genetic variation significantly correlated with habitat or geographical distance. However, the correlation between epigenetic variation and habitat or geographical distance was not significant. Our results showed that, in the case of low genetic variation and genetic diversity, epigenetic variation and DNA methylation may provide a basis for the adaptation of D. angustifolia.
Changes in the Plant β-Sitosterol/Stigmasterol Ratio Caused by the Plant Parasitic Nematode Meloidogyne incognita
Authors: Cabianca Alessandro , Müller Laurin , Pawlowski Katharina , Dahlin Paul
Published: February 2021

Sterols play a key role in various physiological processes of plants. Commonly, stigmasterol, β-sitosterol and campesterol represent the main plant sterols, and cholesterol is often reported as a trace sterol. Changes in plant sterols, especially in β-sitosterol/stigmasterol levels, can be induced by different biotic and abiotic factors. Plant parasitic nematodes, such as the root-knot nematode Meloidogyne incognita, are devastating pathogens known to circumvent plant defense mechanisms. In this study, we investigated the changes in sterols of agricultural important crops, Brassica juncea (brown mustard), Cucumis sativus (cucumber), Glycine max (soybean), Solanum lycopersicum (tomato) and Zea mays (corn), 21 days post inoculation (dpi) with M. incognita. The main changes affected the β-sitosterol/stigmasterol ratio, with an increase of β-sitosterol and a decrease of stigmasterol in S. lycopersicum, G. max, C. sativus and Z. mays. Furthermore, cholesterol levels increased in tomato, cucumber and corn, while cholesterol levels often were below the detection limit in the respective uninfected plants. To better understand the changes in the β-sitosterol/stigmasterol ratio, gene expression analysis was conducted in tomato cv. Moneymaker for the sterol 22C-desaturase gene CYP710A11, responsible for the conversion of β-sitosterol to stigmasterol. Our results showed that the expression of CYP710A11 was in line with the sterol profile of tomato after M. incognita infection. Since sterols play a key role in plant-pathogen interactions, this finding opens novel insights in plant nematode interactions.
Dynamic–Mechanical and Decomposition Properties of Flax/Basalt Hybrid Laminates Based on an Epoxidized Linseed Oil Polymer
Authors: Motoc Dana Luca , Ferri Jose Miguel , Ferrandiz-Bou Santiago , Garcia-Garcia Daniel , Balart Rafael
Published: February 2021

This contribution focuses on the development of flax and flax/basalt hybrid reinforced composites based on epoxidized linseed oil (ELO) resin, exploiting the feasibility of different ratios of glutaric anhydride (GA) to maleinized linseed oil (MLO) in the hardener system (50:0, 40:10 and 30:20 wt.%) to provide crosslinked thermosets with balanced properties. The hybrid laminates have been manufactured by resin transfer molding (RTM) and subjected to dynamic–mechanical (DMA) and thermal gravimetry (TGA) analysis. The presence of glutaric anhydride (GA) resulted in hard and relatively brittle flax and flax/basalt laminates, whose loss moduli decreased as the number of basalt plies diminished. Furthermore, the increase in MLO content in the GA:MLO hardener system shifted the glass transition temperatures (Tg) from 70 °C to 59 and 56 °C, which is representative of a decrease in brittleness of the crosslinked resin. All samples exhibited two stages of their decomposition process irrespective of the MLO content. The latter influenced the residual mass content that increased with the increase of the MLO wt.% from 10 to 30 wt.%, with shifts of the final degradation temperatures from 410 °C to 425 °C and 445 °C, respectively.
A 3D Bioprinted Material That Recapitulates the Perivascular Bone Marrow Structure for Sustained Hematopoietic and Cancer Models
Authors: Moore Caitlyn A. , Siddiqui Zain , Carney Griffin J. , Naaldijk Yahaira , Guiro Khadidiatou , Ferrer Alejandra I. , Sherman Lauren S. , Guvendiren Murat , Kumar Vivek A. , Rameshwar Pranela
Published: February 2021

Translational medicine requires facile experimental systems to replicate the dynamic biological systems of diseases. Drug approval continues to lag, partly due to incongruencies in the research pipeline that traditionally involve 2D models, which could be improved with 3D models. The bone marrow (BM) poses challenges to harvest as an intact organ, making it difficult to study disease processes such as breast cancer (BC) survival in BM, and to effective evaluation of drug response in BM. Furthermore, it is a challenge to develop 3D BM structures due to its weak physical properties, and complex hierarchical structure and cellular landscape. To address this, we leveraged 3D bioprinting to create a BM structure with varied methylcellulose (M): alginate (A) ratios. We selected hydrogels containing 4% (w/v) M and 2% (w/v) A, which recapitulates rheological and ultrastructural features of the BM while maintaining stability in culture. This hydrogel sustained the culture of two key primary BM microenvironmental cells found at the perivascular region, mesenchymal stem cells and endothelial cells. More importantly, the scaffold showed evidence of cell autonomous dedifferentiation of BC cells to cancer stem cell properties. This scaffold could be the platform to create BM models for various diseases and also for drug screening.
Design and Characterization of the Surface Porous UHMWPE Composite Reinforced by Graphene Oxide
Authors: Chen Xiaohong , Zhang Sheng , Zhang Lin , Zhu Ping , Zhang Gangqiang
Published: February 2021

The surface porous ultrahigh molecular weight polyethylene (UHMWPE) composites were successfully fabricated with NaCl and graphene oxide (GO) in the hot-pressing procedure. The GO sheets were evenly dispersed in UHMWPE with the sedimentation method of GO in saturated NaCl. The morphologies, chemical compositions, mechanical, and tribological properties of GO and surface porous GO/NaCl/UHMWPE were investigated. The results show that GO sheet and NaCl could be evenly dispersed in UHMWPE. The regular pores are present on the surface of UHMWPE after NaCl dissolution in distilled water. The wear resistance properties are improved significantly, and the friction properties increased slightly with the addition of GO and NaCl.
An Optimum Fatigue Design of Polymer Composite Compressed Natural Gas Tank Using Hybrid Finite Element-Response Surface Methods
Authors: Kashyzadeh Kazem Reza , Rahimian Koloor Seyed Saeid , Omidi Bidgoli Mostafa , Petrů Michal , Amiri Asfarjani Alireza
Published: February 2021

The main purpose of this research is to design a high-fatigue performance hoop wrapped compressed natural gas (CNG) composite cylinder. To this end, an optimization algorithm was presented as a combination of finite element simulation (FES) and response surface analysis (RSA). The geometrical model was prepared as a variable wall-thickness following the experimental measurements. Next, transient dynamic analysis was performed subjected to the refueling process, including the minimum and maximum internal pressures of 20 and 200 bar, respectively. The time histories of stress tensor components were extracted in the critical region. Furthermore, RSA was utilized to investigate the interaction effects of various polymer composite shell manufacturing process parameters (thickness and fiber angle) on the fatigue life of polymer composite CNG pressure tank (type-4). In the optimization procedure, four parameters including wall-thickness of the composite shell in three different sections of the CNG tank and fiber angle were considered as input variables. In addition, the maximum principal stress of the component was considered as the objective function. Eventually, the fatigue life of the polymer composite tank was calculated using stress-based failure criterion. The results indicated that the proposed new design (applying optimal parameters) leads to improve the fatigue life of the polymer composite tank with polyethylene liner about 2.4 times in comparison with the initial design.
Electromagnetic Interference Shielding of Metal Coated Ultrathin Nonwoven Fabrics and Their Factorial Design
Authors: Palanisamy Sundaramoorthy , Tunakova Veronika , Hu Shi , Yang Tao , Kremenakova Dana , Venkataraman Mohanapriya , Petru Michal , Militky Jiri
Published: February 2021

Electromagnetic (EM) radiation is everywhere in this world and galaxy in different forms and levels. In some cases, human beings need to protect themselves from electromagnetic radiations and the same thing is also recommended for electronic devices as well. Lots of studies are there on the shielding of electromagnetic radiation interference using metals, polymers, and minerals. For protecting the human being, textile structures are playing the main role. In the textile material structure itself many types are there; each one is having its unique geometrical shape and design. In this work, the copper/nickel-coated ultrathin nonwoven fabric is prepared like a strip. The 3, 6, and 9 mm thick strips are prepared and laid at different gaps, angles, and layered to study the effect of factors on EM shielding effectiveness as per ASTM D4935-10 standard. The design of experiment has been done to analyze the three factors and three levels of the strip properties having an influence on electromagnetic shielding results. From the findings of the design of experiment (DoE) screening design, the factors are the thickness of the strips, the gap between the strips, and the strips laid angle having a statistically significant effect on electromagnetic shielding effectiveness.
Effect of the Coupled Pitch–Yaw Motion on the Unsteady Aerodynamic Performance and Structural Response of a Floating Offshore Wind Turbine
Authors: Chen Ziwen , Wang Xiaodong , Kang Shun
Published: February 2021

The floating offshore wind turbines (FOWTs) have many more advantages than the onshore wind turbines, but they also have more complicated aerodynamic characteristics due to complex platform motions. The research objective of this paper is to investigate unsteady aerodynamic characteristics of a FOWT under the pitch, yaw, and coupled pitch–yaw platform motions using the computational fluid dynamics (CFD) method in the Unsteady Reynolds Averaged Navier-Stokes (URANS) simulations. The pitch, yaw, and coupled pitch–yaw motions are studied separately to analyze the platform motions’ effects on the rotor blade. The accuracy of the numerical simulation method is verified, and the overall performances, including power and thrust, are discussed. In addition, the comparison of total aerodynamic performance, force coefficients at different spans and structural dynamic response is provided. The numerical simulation results show that the platform pitching is the main influencing factor of power fluctuation, and the average thrust values of the pitch, yaw, and coupled motions are consistent regardless of the frequency value. The angle of attack (AOA) of airfoils is much more sensitive to the yaw motion, while the blade normal and tangential forces are mainly affected by pitch motion. As for the structural response, the results suggest that the aerodynamic loads of the wind turbine are more sensitive to the pitch motion, which is confirmed by the thrust force and torque of each blade during the platform motions.
A Study of Catechin Photostability Using Photolytic Processing
Authors: Yuann Jeu-Ming P. , Lee Shwu-Yuan , Yang Meei-Ju , Huang Shiuh-Tsuen , Cheng Chien-Wei , Liang Ji-Yuan
Published: February 2021

Catechin exhibits numerous physiological characteristics. In this study, we determined the photosensitivity of catechin to various lights under alkaline conditions, and the mechanisms by which catechin generates free radical species and polymerizes via a photoreaction. In addition to this, the application of catechin photolysis was investigated. A solution of catechin is transparent, but turns yellowish under blue light illumination (BLI) in neutral or weak alkaline solutions. When catechin is subjected to BLI, a dimeric catechin (proanthocyanidin) and a superoxide anion radical (O2•−) are generated in a photolytic reaction. When ascorbic acid or gallic acid is added to catechin and the mixture is subjected to BLI at alkaline pH, fewer catechin dimers and less O2•− are produced, because both acids inhibit the photosensitive oxidation of catechin. When AlCl3 is added to catechin and the mixture is subjected to BLI at pH 8, a photolytic reaction is suppressed by AlCl3, and AlCl3 acts as a catalyst for the disconnection of proanthocyanidin during photolysis. Under alkaline conditions, catechin generates O2•− via photosensitive oxidation, which suppresses the growth of Acinetobacter baumannii (A. baumannii) by at least 4 logs, and deactivates its multi-drug-resistant strain. This study shows that catechin photolysis is a process of oxidation, and that it can be safely applied as a tool for environmental applications.
Microencapsulation of Flaxseed Oil—State of Art
Authors: Yakdhane Asma , Labidi Sabrine , Chaabane Donia , Tolnay Anita , Nath Arijit , Koris András , Vatai Gyula
Published: February 2021

Microencapsulation is a well-known technology for the lipid delivery system. It prevents the oxidation of fatty acids and maintains the quality of lipid after extraction from oil seed and processing. In flaxseed oil, the amount of ω-3 and ω-6 polyunsaturated fatty acids are 39.90–60.42% and 12.25–17.44%, respectively. A comprehensive review article on the microencapsulation of flaxseed oil has not been published yet. Realizing the great advantages of flaxseed oil, information about different technologies related to the microencapsulation of flaxseed oil and their characteristics are discussed in a comprehensive way, in this review article. To prepare the microcapsule of flaxseed oil, an emulsion of oil-water is performed along with a wall material (matrix), followed by drying with a spray-dryer or freeze-dryer. Different matrices, such as plant and animal-based proteins, maltodextrin, gum Arabic, and modified starch are used for the encapsulation of flaxseed oil. In some cases, emulsifiers, such as Tween 80 and soya lecithin are used to prepare flaxseed oil microcapsules. Physico-chemical and bio-chemical characteristics of flaxseed oil microcapsules depend on process parameters, ratio of oil and matrix, and characteristics of the matrix. As an example, the size of the microcapsule, prepared with spray-drying and freeze-drying ranges between 10–400 and 20–5000 μm, respectively. It may be considered that the comprehensive information on the encapsulation of flaxseed oil will boost the development of functional foods and biopharmaceuticals.
Permeability Evolution of Naturally Fractured Coal Injected with High-Temperature Nitrogen: Experimental Observations
Authors: Wang Shengcheng , Li Haijian , Huang Lanying
Published: February 2021

The permeability of more than 70% of coal seams in China is less than 1 mD, creating difficulties in recovering underground coal methane. Therefore, a new technology of high-temperature nitrogen (HTN2) injection into the coal seam was proposed to improve the coal permeability and gas extraction rate. In this paper, the effects of the N2 temperature, injection pressure and cycle number on the permeability of naturally fractured coking coal has been investigated. When HTN2 was injected into coal samples, the results indicated that the permeability decreased over time in the beginning, suddenly increased to a large value, and was subsequently maintained in a relatively stable range. The maximum permeability ratio increased with the rise of the N2 temperature and injection pressure. An analysis indicated that the increase of coal permeability was the result of the increase of the global coal strain caused by thermal expansion and the adsorption-induced expansion. The maximum permeability ratios in various cycles of multicycle N2 injection into the coal sample were all greater than 1.0 while progressively declining. Obviously, the alternating stress was conducive to the further expansion of the coal fractures to increase the coal permeability. However, on the basis of the first period of expansion, re-expansion was difficult and required more energy. The effects of multicycle N2 injection on coal permeability have been considerably improved when compared with N2 injection with only one cycle. The research results are helpful for rapidly extracting methane and guaranteeing mine safety.
Blended Sewage Sludge–Palm Kernel Expeller to Enhance the Palatability of Black Soldier Fly Larvae for Biodiesel Production
Authors: Raksasat Ratchaprapa , Kiatkittipong Kunlanan , Kiatkittipong Worapon , Wong Chung Yiin , Lam Man Kee , Ho Yeek Chia , Oh Wen Da , Suryawan I Wayan Koko , Lim Jun Wei
Published: February 2021

Black soldier fly larvae (BSFL) have been employed for valorizing organic waste materials as the larvae are able to consume organic waste and transform it into valuable larval biomass. In this study, BSFL were found to potentially reduce blended sewage sludge. The addition of palm kernel expeller (PKE) fortified the protein and lipid content in blended sewage sludge substrates, leading to larval growth enhancement. In addition, the larval weight also influenced the lipid yield and fatty acid methyl ester (FAME) profile. However, the optimum ratio of sewage sludge to PKE had to be determined as excess PKE content could become a threat to larval growth by contributing to the reduction of non-fiber carbohydrates content in the feed, thereby resulting in the decrease in lipid yield and FAME content. In this work, a sewage sludge to PKE ratio of 2:3 proffered the highest larval weight gained at 46.99 ± 2.09 mg/larva. Meanwhile, a proportion of 3:2 of sewage sludge to PKE was able provide the highest lipid yield of 17 ± 1.77%. Furthermore, the FAME profile revealed the presence of a significant amount of saturated and monosaturated fatty acids, indicating a good quality biodiesel. Thus, BSFL-based biodiesel fed with blended sewage sludge and PKE could be utilized for producing a high quality biodiesel. However, further improvement on the amount of lipid yield and FAME content should be further investigated.
Therapeutic Role of Nystatin Added to Tissue Conditioners for Treating Denture-Induced Stomatitis: A Systematic Review
Authors: Shaikh Muhammad , Alnazzawi Ahmad , Habib Syed , Lone Mohid , Zafar Muhammad
Published: February 2021

Denture-induced stomatitis (DIS) represents a pathological condition caused by ill-fitting dentures trauma, manifested as inflammation of the palatal tissue beneath the denture. The fungal infections are the principal contributory factors to DIS. Although the aetiology of DIS is multifactorial, the Candida albicans fungal infection remains the main cause. The objective of the present systematic review was to evaluate the latest literature on the addition of nystatin into the tissue conditioners (TCs) in the management of DIS. To search the published literature about “the addition of nystatin in TCs for treating DIS”, electronic databases (PubMed (National library of medicine) and Cochrane Library (Wiley)) were searched (from 1975 until December 2020) using different keywords. Various studies described the effectiveness and efficacy of incorporating nystatin into several TCs. The present systematic review reported that the addition of nystatin is beneficial, with slight or no consequences on both the mechanical and physical features of TCs. Adding nystatin to various TCs for treating DIS can be suggested.
Preprints in Chemistry: An Exploratory Analysis of Differences with Journal Articles
Authors: Pagliaro Mario
Published: February 2021

The exploratory analysis of the differences between preprints and the corresponding peer reviewed journal articles for ten studies first published on ChemRxiv and on Preprints, though statistically non-significant, suggests outcomes of relevance for chemistry researchers and educators. The full transition to open science requires new education of doctoral students and young researchers on scholarly communication in the digital age. The preliminary findings of this study will contribute to inform the curriculum of the aforementioned new courses for young chemists, eventually promoting accelerated innovation in a science that, unique amid all basic sciences, originates a huge industry central to the wealth of nations.
Late Middle—Early Late Pleistocene Mammoths from the Lower Don River Region (Russia)
Authors: Baigusheva Vera S. , Titov Vadim V.
Published: February 2021

The remains of “early” mammoths from a number of localities of the late Middle—early Late Pleistocene on the territory of the South of European Russia (the basin of the Don River, Rostov Region) are described. The description of the teeth and bones of a postcranial skeleton is given. Teeth characteristics (number of plates, lamellar frequency and enamel thickness) allow determining the finds as Mammuthus intermedius, described from the territory of France but known from other regions of Western Europe and Western Siberia as well. In Eastern Europe, this form was a typical representative of the Khazarian theriocomplex and existed during the MIS 5–7 interval. This mammoth taxon differs noticeably from the typical woolly mammoth M. primigenius, which appeared in continental Europe during MIS 4.
Religion and Labor Market Performance: Is It What You Believe or How Much?
Authors: Öhlmann Philipp
Published: February 2021

A growing corpus of literature has explored the influence of religion on economic attitudes and behavior. The present paper investigates the effect of religion on labor market performance using a novel approach to control for the endogeneity of religion. It proposes contingency experience, individual experiences of existential insecurity, as an instrumental variable of a person’s religiosity. The empirical analysis uses data from a household survey in South Africa specifically designed for this study. The econometric approach is the estimation of instrumental variable ordered probit and linear probability models. Using the Centrality of Religiosity Scale (CRS), the analysis differentiates between effects of individual religious intensity and of religious affiliation. The findings show that individual religiosity, measured in the CRS, has a robust and positive effect on labor market performance. Religious affiliation does not seem to affect labor market performance. The positive effect on religiosity is documented in a set of ordered and binary outcome models across different indicators of labor market performance. The study concludes that the intensity of belief exerts an influence on labor market attitudes and outcomes, while affiliation in religious communities (indicating different content of belief) does not seem to make a difference.
Religious Freedom in the Time of the Pandemic
Authors: Mazurkiewicz Piotr
Published: February 2021

The subject of the paper is the issue of restrictions on religious freedom during the COVID-19 pandemic imposed by European countries. The period under examination covers the interval from March to December 2020. The issue is analyzed from the point of view of respecting human rights in a situation of conflict between the values of public health and religious freedom. In this context, the perception of importance and urgency regarding the values that should be protected and the concept of “essential goods”, which are understood differently in secular and religious perspectives, are of particular importance. Another essential issue is not only the scope but also the “depth” of state intervention in the life of religious communities. In Europe, there was a wide variety of national approaches to restricting religious freedom in order to ensure public health. Some of them pursued a very restrictive policy in this area, others moderate, and others very soft. One also could observe the difference in decisions made by most countries during the first and the second waves of the pandemic. A significant element enabling a possible evaluation of the applied solutions is the matter of their duration. Are the introduced limitations only temporary, implemented due to the extraordinary situation, or should they be view as a part of a “radical political experiment”, as a result of which the very understanding of religious freedom and its place in the hierarchy of human rights will change. The arguments of a philosophical, theological, legal and sociological nature are analyzed issuing methods appropriate to each of these disciplines.
Carrots or Sticks: Which Policies Matter the Most in Sustainable Resource Management?
Authors: Costa Joana
Published: February 2021

Green growth has resulted from resource management, setting the speed for sustainable development. Eco-innovations are essential for the improvement of a firm’s performance with societal gains, demanding special attention from policy makers. This paper deals with the effect of policy actions on the enhancement of eco-innovation adoption. The Community Innovation Survey (CIS) 2012–2014 is used to estimate the impact of ‘carrots’ and ‘sticks’ on innovations with ecological benefits. In addition, the impact of a firm’s structural characteristics in ecological strategies is investigated. Regulations and taxes enhance eco-innovation, but grants are only relevant in the case of eco-innovations with external benefits. The firm dimension and non-technological innovation also increase the eco-innovation propensity. Embedding policy actions with environmental concerns will enhance social responsibility and promote resource preservation, providing waste as an economic value. The purpose of this paper is twofold. First, it aims to appraise the effectiveness of the different policy instruments applied in the adoption of innovation with ecological benefits with both internal and external benefits. Secondly, it aims to identify which firm characteristics determine these managerial strategies. Hopefully, light will be cast on the topic so that public and private decision-makers will be given recommendations for policy package design working towards smart and green growth.
Voxel-Based Automatic Tree Detection and Parameter Retrieval from Terrestrial Laser Scans for Plot-Wise Forest Inventory
Authors: Brolly Gábor , Király Géza , Lehtomäki Matti , Liang Xinlian
Published: February 2021

This paper presents a fully automatic method addressing tree mapping and parameter extraction (tree position, stem diameter at breast height, stem curve, and tree height) from terrestrial laser scans in forest inventories. The algorithm is designed to detect trees of various sizes and architectures, produce smooth yet accurate stem curves, and achieve tree height estimates in multi-layered stands, all without employing constraints on the shape of the crown. The algorithm also aims to balance estimation accuracy and computational complexity. The method’s tree detection combines voxel operations and stem surface filtering based on scanning point density. Stem diameters are obtained by creating individual taper models, while tree heights are estimated from the segmentation of tree crowns in the voxel-space. Twenty-four sample plots representing diverse forest structures in the south boreal region of Finland have been assessed from single- and multiple terrestrial laser scans. The mean percentages of completeness in stem detection over all stand complexity categories are 50.9% and 68.5% from single and multiple scans, respectively, while the mean root mean square error (RMSE) of the stem curve estimates ranges from ±1.7 to ±2.3 cm, all of which demonstrates the robustness of the algorithm. Efforts were made to accurately locate tree tops by segmenting individual crowns. Nevertheless, with a mean bias of −2.9 m from single scans and −1.3 m from multiple scans, the algorithm proved conservative in tree height estimates.
GNSS RUMS: GNSS Realistic Urban Multiagent Simulator for Collaborative Positioning Research
Authors: Zhang Guohao , Xu Bing , Ng Hoi-Fung , Hsu Li-Ta
Published: February 2021

Accurate localization of road agents (GNSS receivers) is the basis of intelligent transportation systems, which is still difficult to achieve for GNSS positioning in urban areas due to the signal interferences from buildings. Various collaborative positioning techniques were recently developed to improve the positioning performance by the aid from neighboring agents. However, it is still challenging to study their performances comprehensively. The GNSS measurement error behavior is complicated in urban areas and unable to be represented by naive models. On the other hand, real experiments requiring numbers of devices are difficult to conduct, especially for a large-scale test. Therefore, a GNSS realistic urban measurement simulator is developed to provide measurements for collaborative positioning studies. The proposed simulator employs a ray-tracing technique searching for all possible interferences in the urban area. Then, it categorizes them into direct, reflected, diffracted, and multipath signal to simulate the pseudorange, C/N0, and Doppler shift measurements correspondingly. The performance of the proposed simulator is validated through real experimental comparisons with different scenarios based on commercial-grade receivers. The proposed simulator is also applied with different positioning algorithms, which verifies it is sophisticated enough for the collaborative positioning studies in the urban area.
Long-Term Variations of Plasmaspheric Total Electron Content from Topside GPS Observations on LEO Satellites
Authors: Jin Shuanggen , Gao Chao , Yuan Liangliang , Guo Peng , Calabia Andres , Ruan Haibing , Luo Peng
Published: February 2021

The plasmasphere is located above the ionosphere with low-energy plasma, which is an important component of the solar-terrestrial space environment. As the link between the ionosphere and the magnetosphere, the plasmasphere plays an important role in the coupling process. Therefore, it is of great significance to study the electron content variation of the plasmasphere for the solar-terrestrial space environment. Nowadays, the topside global positioning system (GPS) observations on Low Earth Orbit (LEO) satellites provide a unique opportunity to estimate and study variations in the plasmasphere. In this paper, the plasmaspheric total electron content (PTEC) is estimated, and its long-term variations are studied from topside GPS observations onboard the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC). The PTEC in the daytime is higher than that in the nighttime, with the peak between 14:00 and 17:00 in the magnetic local time, while the minimum value of PTEC in the belt appears between 3:00 and 6:00 in the magnetic local time before sunrise. For seasonal variations, the PTEC is the highest in spring of the northern hemisphere and the lowest in summer of the northern hemisphere regardless of the state of the solar activity. The long-term variation in PTEC is further analyzed using 11-year COSMIC GPS observation data from 2007 to 2017. A high correlation between PTEC and the F10.7 indices is found. Particularly in the geomagnetic high-latitude region during the daytime, the correlation coefficient reaches 0.93. The worst case occurs during the nighttime in the geomagnetic middle-latitude region, but the correlation coefficient is still higher than 0.88. The long-term variations of plasmaspheric TEC are mainly related to the solar activity.
Data Augmentation and Spectral Structure Features for Limited Samples Hyperspectral Classification
Authors: Wang Wenning , Liu Xuebin , Mou Xuanqin
Published: February 2021

For both traditional classification and current popular deep learning methods, the limited sample classification problem is very challenging, and the lack of samples is an important factor affecting the classification performance. Our work includes two aspects. First, the unsupervised data augmentation for all hyperspectral samples not only improves the classification accuracy greatly with the newly added training samples, but also further improves the classification accuracy of the classifier by optimizing the augmented test samples. Second, an effective spectral structure extraction method is designed, and the effective spectral structure features have a better classification accuracy than the true spectral features.
Semi-Supervised Multi-Temporal Deep Representation Fusion Network for Landslide Mapping from Aerial Orthophotos
Authors: Zhang Xiaokang , Pun Man-On , Liu Ming
Published: February 2021

Using remote sensing techniques to monitor landslides and their resultant land cover changes is fundamentally important for risk assessment and hazard prevention. Despite enormous efforts in developing intelligent landslide mapping (LM) approaches, LM remains challenging owing to high spectral heterogeneity of very-high-resolution (VHR) images and the daunting labeling efforts. To this end, a deep learning model based on semi-supervised multi-temporal deep representation fusion network, namely SMDRF-Net, is proposed for reliable and efficient LM. In comparison with previous methods, the SMDRF-Net possesses three distinct properties. (1) Unsupervised deep representation learning at the pixel- and object-level is performed by transfer learning using the Wasserstein generative adversarial network with gradient penalty to learn discriminative deep features and retain precise outlines of landslide objects in the high-level feature space. (2) Attention-based adaptive fusion of multi-temporal and multi-level deep representations is developed to exploit the spatio-temporal dependencies of deep representations and enhance the feature representation capability of the network. (3) The network is optimized using limited samples with pseudo-labels that are automatically generated based on a comprehensive uncertainty index. Experimental results from the analysis of VHR aerial orthophotos demonstrate the reliability and robustness of the proposed approach for LM in comparison with state-of-the-art methods.
Airborne Radar Super-Resolution Imaging Based on Fast Total Variation Method
Authors: Zhang Qiping , Zhang Yin , Zhang Yongchao , Huang Yulin , Yang Jianyu
Published: February 2021

Total variation (TV) is an effective super-resolution method to improve the azimuth resolution and preserve the contour information of the target in airborne radar imaging. However, the computational complexity is very high because of the matrix inversion, reaching O(N3). In this paper, a Gohberg–Semencul (GS) representation based fast TV (GSFTV) method is proposed to make up for the shortcoming. The proposed GSFTV method fist utilizes a one-dimensional TV norm as the regular term under regularization framework, which is conducive to achieve super-resolution while preserving the target contour. Then, aiming at the very high computational complexity caused by matrix inversion when minimizing the TV regularization problem, we use the low displacement rank feature of Toeplitz matrix to achieve fast inversion through GS representation. This reduces the computational complexity from O(N3) to O(N2), benefiting efficiency improvement for airborne radar imaging. Finally, the simulation and real data processing results demonstrate that the proposed GSFTV method can simultaneously improve the resolution and preserve the target contour. Moreover, the very high computational efficiency of the proposed GSFTV method is tested by hardware platform.
Using a System-Based Monitoring Paradigm to Assess Fatigue during Submaximal Static Exercise of the Elbow Extensor Muscles
Authors: Madden Kaci E. , Djurdjanovic Dragan , Deshpande Ashish D.
Published: February 2021

Current methods for evaluating fatigue separately assess intramuscular changes in individual muscles from corresponding alterations in movement output. The purpose of this study is to investigate if a system-based monitoring paradigm, which quantifies how the dynamic relationship between the activity from multiple muscles and force changes over time, produces a viable metric for assessing fatigue. Improvements made to the paradigm to facilitate online fatigue assessment are also discussed. Eight participants performed a static elbow extension task until exhaustion, while surface electromyography (sEMG) and force data were recorded. A dynamic time-series model mapped instantaneous features extracted from sEMG signals of multiple synergistic muscles to extension force. A metric, called the Freshness Similarity Index (FSI), was calculated using statistical analysis of modeling errors to reveal time-dependent changes in the dynamic model indicative of performance degradation. The FSI revealed strong, significant within-individual associations with two well-accepted measures of fatigue, maximum voluntary contraction (MVC) force (rrm=−0.86) and ratings of perceived exertion (RPE) (rrm=0.87), substantiating the viability of a system-based monitoring paradigm for assessing fatigue. These findings provide the first direct and quantitative link between a system-based performance degradation metric and traditional measures of fatigue.
Applying Learning Analytics to Detect Sequences of Actions and Common Errors in a Geometry Game
Authors: Gomez Manuel J. , Ruipérez-Valiente José A. , Martínez Pedro A. , Kim Yoon Jeon
Published: February 2021

Games have become one of the most popular activities across cultures and ages. There is ample evidence that supports the benefits of using games for learning and assessment. However, incorporating game activities as part of the curriculum in schools remains limited. Some of the barriers for broader adoption in classrooms is the lack of actionable assessment data, the fact that teachers often do not have a clear sense of how students are interacting with the game, and it is unclear if the gameplay is leading to productive learning. To address this gap, we seek to provide sequence and process mining metrics to teachers that are easily interpretable and actionable. More specifically, we build our work on top of Shadowspect, a three-dimensional geometry game that has been developed to measure geometry skills as well other cognitive and noncognitive skills. We use data from its implementation across schools in the U.S. to implement two sequence and process mining metrics in an interactive dashboard for teachers. The final objective is to facilitate that teachers can understand the sequence of actions and common errors of students using Shadowspect so they can better understand the process, make proper assessment, and conduct personalized interventions when appropriate.
A Spatiotemporal-Oriented Deep Ensemble Learning Model to Defend Link Flooding Attacks in IoT Network
Authors: Chen Yen-Hung , Lai Yuan-Cheng , Jan Pi-Tzong , Tsai Ting-Yi
Published: February 2021

(1) Background: Link flooding attacks (LFA) are a spatiotemporal attack pattern of distributed denial-of-service (DDoS) that arranges bots to send low-speed traffic to backbone links and paralyze servers in the target area. (2) Problem: The traditional methods to defend against LFA are heuristic and cannot reflect the changing characteristics of LFA over time; the AI-based methods only detect the presence of LFA without considering the spatiotemporal series attack pattern and defense suggestion. (3) Methods: This study designs a deep ensemble learning model (Stacking-based integrated Convolutional neural network–Long short term memory model, SCL) to defend against LFA: (a) combining continuous network status as an input to represent “continuous/combination attacking action” and to help CNN operation to extract features of spatiotemporal attack pattern; (b) applying LSTM to periodically review the current evolved LFA patterns and drop the obsolete ones to ensure decision accuracy and confidence; (c) stacking System Detector and LFA Mitigator module instead of only one module to couple with LFA detection and mediation at the same time. (4) Results: The simulation results show that the accuracy rate of SCL successfully blocking LFA is 92.95%, which is 60.81% higher than the traditional method. (5) Outcomes: This study demonstrates the potential and suggested development trait of deep ensemble learning on network security.
A Step-by-Step Damage Identification Method Based on Frequency Response Function and Cross Signature Assurance Criterion
Authors: Zhan Jiawang , Zhang Fei , Siahkouhi Mohammad
Published: February 2021

This paper aims to present a method for quantitative damage identification of a simply supported beam, which integrates the frequency response function (FRF) and model updating. The objective function is established using the cross-signature assurance criterion (CSAC) indices of the FRFs between the measurement points and the natural frequency. The CSAC index in the frequency range between the first two frequencies is found to be sensitive to damage. The proposed identification procedure is tried to identify the single and multiple damages. To verify the effectiveness of the method, numerical simulation and laboratory testing were conducted on some model steel beams with simulated damage by cross-cut sections, and the identification results were compared with the real ones. The analysis results show that the proposed damage evaluation method is insensitive to the systematic test errors and is able to locate and quantify the damage within the beam structures step by step.
A Generalization Performance Study Using Deep Learning Networks in Embedded Systems
Authors: Gorospe Joseba , Mulero Rubén , Arbelaitz Olatz , Muguerza Javier , Antón Miguel Ángel
Published: February 2021

Deep learning techniques are being increasingly used in the scientific community as a consequence of the high computational capacity of current systems and the increase in the amount of data available as a result of the digitalisation of society in general and the industrial world in particular. In addition, the immersion of the field of edge computing, which focuses on integrating artificial intelligence as close as possible to the client, makes it possible to implement systems that act in real time without the need to transfer all of the data to centralised servers. The combination of these two concepts can lead to systems with the capacity to make correct decisions and act based on them immediately and in situ. Despite this, the low capacity of embedded systems greatly hinders this integration, so the possibility of being able to integrate them into a wide range of micro-controllers can be a great advantage. This paper contributes with the generation of an environment based on Mbed OS and TensorFlow Lite to be embedded in any general purpose embedded system, allowing the introduction of deep learning architectures. The experiments herein prove that the proposed system is competitive if compared to other commercial systems.
Deep Learning Approaches on Defect Detection in High Resolution Aerial Images of Insulators
Authors: Wen Qiaodi , Luo Ziqi , Chen Ruitao , Yang Yifan , Li Guofa
Published: February 2021

By detecting the defect location in high-resolution insulator images collected by unmanned aerial vehicle (UAV) in various environments, the occurrence of power failure can be timely detected and the caused economic loss can be reduced. However, the accuracies of existing detection methods are greatly limited by the complex background interference and small target detection. To solve this problem, two deep learning methods based on Faster R-CNN (faster region-based convolutional neural network) are proposed in this paper, namely Exact R-CNN (exact region-based convolutional neural network) and CME-CNN (cascade the mask extraction and exact region-based convolutional neural network). Firstly, we proposed an Exact R-CNN based on a series of advanced techniques including FPN (feature pyramid network), cascade regression, and GIoU (generalized intersection over union). RoI Align (region of interest align) is introduced to replace RoI pooling (region of interest pooling) to address the misalignment problem, and the depthwise separable convolution and linear bottleneck are introduced to reduce the computational burden. Secondly, a new pipeline is innovatively proposed to improve the performance of insulator defect detection, namely CME-CNN. In our proposed CME-CNN, an insulator mask image is firstly generated to eliminate the complex background by using an encoder-decoder mask extraction network, and then the Exact R-CNN is used to detect the insulator defects. The experimental results show that our proposed method can effectively detect insulator defects, and its accuracy is better than the examined mainstream target detection algorithms.