bluuebunny/crossref_metadata_2025 huggingface dataset:
Conversion of Wheat Bran to Xylanases and Dye Adsorbent by Streptomyces thermocarboxydus
Authors: Tran Thi Ngoc , Doan Chien Thang , Wang San-Lang
Published: January 2021

Agro-byproducts can be utilized as effective and low-cost nutrient sources for microbial fermentation to produce a variety of usable products. In this study, wheat bran powder (WBP) was found to be the most effective carbon source for xylanase production by Streptomyces thermocarboxydus TKU045. The optimal media for xylanase production was 2% (w/v) WBP, 1.50% (w/v) KNO3, 0.05% (w/v) MgSO4, and 0.10% (w/v) K2HPO4, and the optimal culture conditions were 50 mL (in a 250 mL-volume Erlenmeyer flask), initial pH 9.0, 37 °C, 125 rpm, and 48 h. Accordingly, the highest xylanase activity was 6.393 ± 0.130 U/mL, 6.9-fold higher than that from un-optimized conditions. S. thermocarboxydus TKU045 secreted at least four xylanases with the molecular weights of >180, 36, 29, and 27 kDa when cultured on the WBP-containing medium. The enzyme cocktail produced by S. thermocarboxydus TKU045 was optimally active over a broad range of temperature and pH (40–70 °C and pH 5–8, respectively) and could hydrolyze birchwood xylan to produce xylobiose as the major product. The obtained xylose oligosaccharide (XOS) were investigated for 2,2-diphenyl-1-picrylhydrazyl radical scavenging activity and the growth effect of lactic acid bacteria. Finally, the solid waste from the WBP fermentation using S. thermocarboxydus TKU045 revealed the high adsorption of Congo red, Red 7, and Methyl blue. Thus, S. thermocarboxydus TKU045 could be a potential strain to utilize wheat bran to produce xylanases for XOS preparation and dye adsorbent.
Injection Molded PP Foams Using Food Ingredients for Food Packaging Applications
Authors: Tsagdi Artemis , Drossos Ioannis , Georgiou Despoina , Exarhopoulos Stylianos , Karasiotas Georgios , Kallitsis Joannis K. , Kalogianni Eleni P.
Published: January 2021

A new approach to the creation of polypropylene (PP) based foaming materials was developed using food grade foaming agents that were coated on the PP pellets. More specifically, sodium bicarbonate and organic acids were used to coat PP pellets using either polyethyleneoxide (PEO) or lipid esters as coating stabilizers. In order to overcome the problem of the thermal decomposition of sodium bicarbonate at temperatures lower than the PP melting temperature, which makes the direct foaming during melt mixing impossible, the proposed methodology was proved quite efficient. Thus, new PP masterbatches were prepared, where the foaming agents were incorporated as coating at PP pellets at contents up to 10%, and initially used in Lab scale injection machines in order to find the best combination of materials that resulted in the production of foamed articles. Subsequently selected material combinations were tested in an industrial scale injection molding machine, where an optimization of the injection parameters was attempted. The outcome of this was the production of PP articles with significantly increased void fraction, up to 14%, decreased thermal conductivity, up to 20%, and various pore sizes as was observed via microscopic examination using SEM and CLSM.
Synthesis and Characterization of Hydrophobically Modified Xylans
Authors: Cheng Huai N. , Biswas Atanu , Kim Sanghoon , Alves Carlucio R. , Furtado Roselayne F.
Published: January 2021

Xylan is a major type of hemicellulose that has attracted a lot of research and development activities. It is often derivatized in order to improve its properties. In the literature, hydrophobic modification of polymers is often used to produce surfactant-like materials and associative thickeners. In this work, we have derivatized xylan with alkyl ketene dimer (AKD) and two types of alkenyl succinic anhydrides (ASAs). The xylan-AKD derivatives have been made at 90 °C, using dimethyl sulfoxide as solvent and 4-dimethylaminopyridine as promoter. Samples with degrees of substitution (DS) up to 0.006 have been produced. The xylan-ASA derivatives have been synthesized at 120 °C in dimethyl sulfoxide with DS up to 0.105–0.135. The structures of these products have been confirmed with NMR and FT-IR. These xylan derivatives increase the structural diversity of xylan and provide additional options for people seeking to use hydrophobically modified polysaccharides in their applications.
Characterization of Polycaprolactone Nanohydroxyapatite Composites with Tunable Degradability Suitable for Indirect Printing
Authors: Doyle Stephanie E. , Henry Lauren , McGennisken Ellen , Onofrillo Carmine , Bella Claudia Di , Duchi Serena , O'Connell Cathal D. , Pirogova Elena
Published: January 2021

Degradable bone implants are designed to foster the complete regeneration of natural tissue after large-scale loss trauma. Polycaprolactone (PCL) and hydroxyapatite (HA) composites are promising scaffold materials with superior mechanical and osteoinductive properties compared to the single materials. However, producing three-dimensional (3D) structures with high HA content as well as tuneable degradability remains a challenge. To address this issue and create homogeneously distributed PCL-nanoHA (nHA) scaffolds with tuneable degradation rates through both PCL molecular weight and nHA concentration, we conducted a detailed characterisation and comparison of a range of PCL-nHA composites across three molecular weight PCLs (14, 45, and 80 kDa) and with nHA content up to 30% w/w. In general, the addition of nHA results in an increase of viscosity for the PCL-nHA composites but has little effect on their compressive modulus. Importantly, we observe that the addition of nHA increases the rate of degradation compared to PCL alone. We show that the 45 and 80 kDa PCL-nHA groups can be fabricated via indirect 3D printing and have homogenously distributed nHA even after fabrication. Finally, the cytocompatibility of the composite materials is evaluated for the 45 and 80 kDa groups, with the results showing no significant change in cell number compared to the control. In conclusion, our analyses unveil several features that are crucial for processing the composite material into a tissue engineered implant.
Impact of Titanium Dioxide in the Mechanical Recycling of Post-Consumer Polyethylene Terephthalate Bottle Waste: Tensile and Fracture Behavior
Authors: Loaeza David , Cailloux Jonathan , Santana Pérez Orlando , Sánchez-Soto Miguel , Maspoch Maria
Published: January 2021

This work provides an experimental analysis regarding the fracture behavior of recycled opaque PET (rPET-O) containing titanium dioxide (TiO2) under plane stress conditions. For this purpose, a commercially post-consumer transparent colored/opaque PET flakes mix was processed using a semi-industrial extrusion calendering process. The manufactured rPET-O sheets had a TiO2 content of 1.45 wt.%. The mechanical and fracture properties of unaged and physically aged (1 year) samples were determined through uniaxial tensile experiments and the Essential Work of Fracture (EWF) methodology, respectively, and were compared to those of recycled transparent PET (rPET-T). Under tensile loading, independently of the aging time, rPET-O samples exhibited similar mechanical behavior as rPET-T up to the yield point. The main differences remained in the post-yielding region. The presence of TiO2 particles allowed reducing the strain energy density up to neck formation in aged samples. Regarding the EWF analysis, it is argued that the energy consumed up to the onset of crack propagation (we) for rPET-T was mainly dependent of the molecular mobility. That is, the we value decreased by 26% when rPET-T was physically aged. Interestingly, we values remained independent of the aging time for rPET-O. In fact, it was highlighted that before crack propagation, the EWF response was principally governed by matrix cavitation ahead of the crack tip, which allowed a significant release of the triaxial stress state independently of the molecular mobility. This property enabled rPET-O to exhibit a resistance to crack initiation 17% higher as compared to rPET-T when the material was physically aged. Finally, independently of the aging time, rPET-O exhibited a resistance to crack growth approximately 21% larger than rPET-T due to matrix fibrillation in large scale deformation.
Deciphering Structural Determinants in Chondroitin Sulfate Binding to FGF-2: Paving the Way to Enhanced Predictability of Their Biological Functions
Authors: Vessella Giulia , Vázquez José Antonio , Valcárcel Jesús , Lagartera Laura , Monterrey Dianélis T. , Bastida Agatha , García-Junceda Eduardo , Bedini Emiliano , Fernández-Mayoralas Alfonso , Revuelta Julia
Published: January 2021

Controlling chondroitin sulfates (CSs) biological functions to exploit their interesting potential biomedical applications requires a comprehensive understanding of how the specific sulfate distribution along the polysaccharide backbone can impact in their biological activities, a still challenging issue. To this aim, herein, we have applied an “holistic approach” recently developed by us to look globally how a specific sulfate distribution within CS disaccharide epitopes can direct the binding of these polysaccharides to growth factors. To do this, we have analyzed several polysaccharides of marine origin and semi-synthetic polysaccharides, the latter to isolate the structure-activity relationships of their rare, and even unnatural, sulfated disaccharide epitopes. SPR studies revealed that all the tested polysaccharides bind to FGF-2 (with exception of CS-8, CS-12 and CS-13) according to a model in which the CSs first form a weak complex with the protein, which is followed by maturation to tight binding with kD ranging affinities from ~1.31 μM to 130 μM for the first step and from ~3.88 μM to 1.8 nM for the second one. These binding capacities are, interestingly, related with the surface charge of the 3D-structure that is modulated by the particular sulfate distribution within the disaccharide repeating-units.
Fabrication of Drug-Eluting Nano-Hydroxylapatite Filled Polycaprolactone Nanocomposites Using Solution-Extrusion 3D Printing Technique
Authors: Chou Pang-Yun , Chou Ying-Chao , Lai Yu-Hsuan , Lin Yu-Ting , Lu Chia-Jung , Liu Shih-Jung
Published: January 2021

Polycaprolactone/nano-hydroxylapatite (PCL/nHA) nanocomposites have found use in tissue engineering and drug delivery owing to their good biocompatibility with these types of applications in addition to their mechanical characteristics. Three-dimensional (3D) printing of PCL/nHA nanocomposites persists as a defiance mostly because of the lack of commercial filaments for the conventional fused deposition modeling (FDM) method. In addition, as the composites are prepared using FDM for the purpose of delivering pharmaceuticals, thermal energy can destroy the embedded drugs and biomolecules. In this report, we investigated 3D printing of PCL/nHA using a lab-developed solution-extrusion printer, which consists of an extrusion feeder, a syringe with a dispensing nozzle, a collection table, and a command port. The effects of distinct printing variables on the mechanical properties of nanocomposites were investigated. Drug-eluting nanocomposite screws were also prepared using solution-extrusion 3D printing. The empirical outcomes suggest that the tensile properties of the 3D-printed PCL/nHA nanocomposites increased with the PCL/nHA-to-dichloromethane (DCM) ratio, fill density, and print orientation but decreased with an increase in the moving speed of the dispensing tip. Furthermore, printed drug-eluting PCL/nHA screws eluted high levels of antimicrobial vancomycin and ceftazidime over a 14-day period. Solution-extrusion 3D printing demonstrated excellent capabilities for fabricating drug-loaded implants for various medical applications.
Design and Evaluation of a New Natural Multi-Layered Biopolymeric Adsorbent System-Based Chitosan/Cellulosic Nonwoven Material for the Biosorption of Industrial Textile Effluents
Authors: EL-Ghoul Yassine , Ammar Chiraz , Alminderej Fahad M. , Shafiquzzaman Md.
Published: January 2021

The adsorption phenomenon using low-cost adsorbents that are abundant in nature is of great interest when the adsorbed capacity is significant. A newly designed natural polyelectrolyte multi-layered (PEM) biopolymeric system-based chitosan/modified chitosan polymer and functionalized cellulosic nonwoven material was prepared and used as an effective adsorbent for Reactive Red 198 (RR198) dye solutions. The bio-sorbent was characterized by FTIR, SEM, and thermal (TGA/DTA) analysis. The swelling behavior was also evaluated, showing the great increase of the hydrophilicity of the prepared adsorbent biopolymer. The effect of various process parameters on the performance of RR198 dye removal such as pH, contact time, temperature, and initial dye concentration was studied. The biopolymeric system has shown good efficiency of adsorption compared to other adsorbents based on chitosan polymer. The highest adsorption capacity was found to be 722.3 mgg−1 at pH = 4 (ambient temperature, time = 120 min and dye concentration = 600 mg L−1). The adsorption process fitted well to both pseudo-second-order kinetics and Freundlich/Temkin adsorption isotherm models. Regarding its low cost, easy preparation, and promising efficient adsorption results, this new concepted multi-layered bio-sorbent could be an effective solution for the treatment of industrial wastewater.
People Love Their Religion: Political Conflict on Religion in Early Independent Mexico
Authors: Hensel Silke
Published: January 2021

Global histories commonly attribute the secularization of the state exclusively to Europe. However, the church state conflict over these issues has been an important thread in much of Latin America. In Mexico, questions about the role of religion and the church in society became a major political conflict after independence. Best known for the Mexican case are the disputes over the constitution of 1857, which laid down the freedom of religion, and the Cristero Revolt in the 1920s. However, the history of struggles over secularization goes back further. In 1835, the First Republic ultimately failed, because of the massive protests against the anticlerical laws of the government. In the paper, this failure is understood as a genuine religious conflict over the question of the proper social and political order, in which large sections of the population were involved. Beginning with the anticlerical laws of 1833, political and religious reaction in Mexico often began with a pronunciamiento (a mixture of rebellion and petitioning the authorities) and evolved into conflicts over federalism vs. centralism.
Essential Oils and Their Application on Active Packaging Systems: A Review
Authors: Carpena Maria , Nuñez-Estevez Bernabe , Soria-Lopez Anton , Garcia-Oliveira Paula , Prieto Miguel A.
Published: January 2021

The food industry is continuously evolving through the application of innovative tools and ingredients towards more effective, safe, natural and ecofriendly solutions to satisfy the demands of the costumers. In this context, natural sources (i.e., leaves, seeds, peels or unused pulp) can entail a valuable source of compounds, such as essential oils (EOs), with recognized antioxidant and antimicrobial properties that can be used as natural additives in packaging applications. The current trend is the incorporation of EOs into diverse kinds of biodegradable materials, such as edible films, thus developing active packaging systems with improved preservation properties that can offer benefits to both the food and packaging industry by reducing food waste and improving the management of packaging waste. EOs may be added into the packaging material as free or encapsulated molecules, where, especially this last option, has been revealed as very promising. The addition of these lipophilic compounds provides to the end-product various bioactivities of interest, which can eventually extend the shelf-life of the product by preventing food spoilage. Pairing biodegradable packaging with EOs extracted from natural agro-industrial by-products can lead to a more sustainable food industry. Recent knowledge and advances on this issue will be reviewed in the present work.
Compression of Remotely Sensed Astronomical Image Using Wavelet-Based Compressed Sensing in Deep Space Exploration
Authors: Zhang Yong , Jiang Jie , Zhang Guangjun
Published: January 2021

Compression of remotely sensed astronomical images is an essential part of deep space exploration. This study proposes a wavelet-based compressed sensing (CS) algorithm for astronomical image compression in a miniaturized independent optical sensor system, which introduces a new framework for CS in the wavelet domain. The algorithm starts with a traditional 2D discrete wavelet transform (DWT), which provides frequency information of an image. The wavelet coefficients are rearranged in a new structured manner determined by the parent–child relationship between the sub-bands. We design scanning modes based on the direction information of high-frequency sub-bands, and propose an optimized measurement matrix with a double allocation of measurement rate. Through a single measurement matrix, higher measurement rates can be simultaneously allocated to sparse vectors containing more information and coefficients with higher energy in sparse vectors. The double allocation strategy can achieve better image sampling. At the decoding side, orthogonal matching pursuit (OMP) and inverse discrete wavelet transform (IDWT) are used to reconstruct the image. Experimental results on simulated image and remotely sensed astronomical images show that our algorithm can achieve high-quality reconstruction with a low measurement rate.
Mapping Seasonal Agricultural Land Use Types Using Deep Learning on Sentinel-2 Image Time Series
Authors: Debella-Gilo Misganu , Gjertsen Arnt Kristian
Published: January 2021

The size and location of agricultural fields that are in active use and the type of use during the growing season are among the vital information that is needed for the careful planning and forecasting of agricultural production at national and regional scales. In areas where such data are not readily available, an independent seasonal monitoring method is needed. Remote sensing is a widely used tool to map land use types, although there are some limitations that can partly be circumvented by using, among others, multiple observations, careful feature selection and appropriate analysis methods. Here, we used Sentinel-2 satellite image time series (SITS) over the land area of Norway to map three agricultural land use classes: cereal crops, fodder crops (grass) and unused areas. The Multilayer Perceptron (MLP) and two variants of the Convolutional Neural Network (CNN), are implemented on SITS data of four different temporal resolutions. These enabled us to compare twelve model-dataset combinations to identify the model-dataset combination that results in the most accurate predictions. The CNN is implemented in the spectral and temporal dimensions instead of the conventional spatial dimension. Rather than using existing deep learning architectures, an autotuning procedure is implemented so that the model hyperparameters are empirically optimized during the training. The results obtained on held-out test data show that up to 94% overall accuracy and 90% Cohen’s Kappa can be obtained when the 2D CNN is applied on the SITS data with a temporal resolution of 7 days. This is closely followed by the 1D CNN on the same dataset. However, the latter performs better than the former in predicting data outside the training set. It is further observed that cereal is predicted with the highest accuracy, followed by grass. Predicting the unused areas has been found to be difficult as there is no distinct surface condition that is common for all unused areas.
Unraveling the Morphological Constraints on Roman Gold Mining Hydraulic Infrastructure in NW Spain. A UAV-Derived Photogrammetric and Multispectral Approach
Authors: Fernández-Lozano Javier , Sanz-Ablanedo Enoc
Published: January 2021

The province of León preserves a unique hydraulic infrastructure 1200 km-long, used for the exploitation of auriferous deposits in Roman times. It represents the most extensive waterworks in Europe and is one of the best-preserved examples of mining heritage in Antiquity. In this work, three mining exploitation sectors (upper, middle, and lower) characterized by channels and leats developed in different geological materials were examined, using Unmanned Aerial Vehicles (UAVs). A multi-approach based on a comparison of photogrammetric and multispectral data improved the identification and description of the hydraulic network. Comparison with traditional orthoimages and LiDAR data suggests that UAV-derived multispectral images are of great interest in areas where these sets of data have low resolution or areas that are densely covered by vegetation. The results showed that the size of the channel box and its width were factors that do not depend exclusively on the available water resources, as previously suggested, but also on the geological and hydraulic conditioning factors that intervene in each sector. Additionally, the detailed study allowed the establishment of a water sheet maximum height that was much lower than previously thought. All in all, these inferences might help researchers develop new strategies for mapping the Roman mining infrastructure and establishing the importance of geological inheritance on the construction of the hydraulic system that led the Romans to the accomplishment of the largest mining infrastructure ever known in Europe.
Imaging Spectroscopy for Conservation Applications
Authors: Seeley Megan , Asner Gregory P.
Published: January 2021

As humans continue to alter Earth systems, conservationists look to remote sensing to monitor, inventory, and understand ecosystems and ecosystem processes at large spatial scales. Multispectral remote sensing data are commonly integrated into conservation decision-making frameworks, yet imaging spectroscopy, or hyperspectral remote sensing, is underutilized in conservation. The high spectral resolution of imaging spectrometers captures the chemistry of Earth surfaces, whereas multispectral satellites indirectly represent such surfaces through band ratios. Here, we present case studies wherein imaging spectroscopy was used to inform and improve conservation decision-making and discuss potential future applications. These case studies include a broad array of conservation areas, including forest, dryland, and marine ecosystems, as well as urban applications and methane monitoring. Imaging spectroscopy technology is rapidly developing, especially with regard to satellite-based spectrometers. Improving on and expanding existing applications of imaging spectroscopy to conservation, developing imaging spectroscopy data products for use by other researchers and decision-makers, and pioneering novel uses of imaging spectroscopy will greatly expand the toolset for conservation decision-makers.
Roll Calibration for CryoSat-2: A Comprehensive Approach
Authors: Garcia-Mondéjar Albert , Scagliola Michele , Gourmelen Noel , Bouffard Jerome , Roca Mònica
Published: January 2021

CryoSat-2 is the first satellite mission carrying a high pulse repetition frequency radar altimeter with interferometric capability on board. Across track interferometry allows the angle to the point of closest approach to be determined by combining echoes received by two antennas and knowledge of their orientation. Accurate information of the platform mispointing angles, in particular of the roll, is crucial to determine the angle of arrival in the across-track direction with sufficient accuracy. As a consequence, different methods were designed in the CryoSat-2 calibration plan in order to estimate interferometer performance along with the mission and to assess the roll’s contribution to the accuracy of the angle of arrival. In this paper, we present the comprehensive approach used in the CryoSat-2 Mission to calibrate the roll mispointing angle, combining analysis from external calibration of both man-made targets, i.e., transponder and natural targets. The roll calibration approach for CryoSat-2 is proven to guarantee that the interferometric measurements are exceeding the expected performance.
A Field Weed Density Evaluation Method Based on UAV Imaging and Modified U-Net
Authors: Zou Kunlin , Chen Xin , Zhang Fan , Zhou Hang , Zhang Chunlong
Published: January 2021

Weeds are one of the main factors affecting the yield and quality of agricultural products. Accurate evaluation of weed density is of great significance for field management, especially precision weeding. In this paper, a weed density calculating and mapping method in the field is proposed. An unmanned aerial vehicle (UAV) was used to capture field images. The excess green minus excess red index, combined with the minimum error threshold segmentation method, was used to segment green plants and bare land. A modified U-net was used to segment crops from images. After removing the bare land and crops from the field, images of weeds were obtained. The weed density was evaluated by the ratio of weed area to total area on the segmented image. The accuracy of the green plant segmentation was 93.5%. In terms of crop segmentation, the intersection over union (IoU) was 93.40%, and the segmentation time of a single image was 35.90 ms. Finally, the determination coefficient of the UAV evaluated weed density and the manually observed weed density was 0.94, and the root mean square error was 0.03. With the proposed method, the weed density of a field can be effectively evaluated from UAV images, hence providing critical information for precision weeding.
Ecological and Environmental Effects of Estuarine Wetland Loss Using Keyhole and Landsat Data in Liao River Delta, China
Authors: Yin Hongyan , Hu Yuanman , Liu Miao , Li Chunlin , Lv Jiujun
Published: January 2021

An estuarine wetland is an area of high ecological productivity and biodiversity, and it is also an anthropic activity hotspot area, which is of concern. The wetlands in estuarine areas have suffered declines, which have had remarkable ecological impacts. The land use changes, especially wetland loss, were studied based on Keyhole and Landsat images in the Liao River delta from 1962 to 2016. The dynamics of the ecosystem service values (ESVs), suitable habitat for birds, and soil heavy metal potential ecological risk were chosen to estimate the ecological effects with the benefit transfer method, synthetic overlaying method, and potential ecological risk index (RI) method, respectively. The driving factors of land use change and ecological effects were analyzed with redundancy analysis (RDA). The results showed that the built-up area increased from 95.98 km2 in 1962 to 591.49 km2 in 2016, and this large change was followed by changes in paddy fields (1351.30 to 1522.39 km2) and dry farmland (189.5 to 294.14 km2). The area of wetlands declined from 1823.16 km2 in 1962 to 1153.52 km2 in 2016, and this change was followed by a decrease in the water area (546.2 to 428.96 km2). The land use change was characterized by increasing built-up (516.25%), paddy fields (12.66%) and dry farmland (55.22%) areas and a decline in the wetland (36.73%) and water areas (21.47%) from 1962–2016. Wetlands decreased by 669.64 km2. The ESV values declined from 6.24 billion US$ to 4.46 billion US$ from 1962 to 2016, which means the ESVs were reduced by 19.26% due to wetlands being cultivated and the urbanization process. The area of suitable habitat for birds decreased by 1449.49 km2, or 61.42% of the total area available in 1962. Cd was the primary soil heavy metal pollutant based on its concentration, accumulation, and potential ecological risk contribution. The RDA showed that the driving factors of comprehensive ecological effects include wetland area, Cd and Cr concentration, river and oil well distributions. This study provides a comprehensive approach for estuarine wetland cultivation and scientific support for wetland conservation.
Pandemic Induced Changes in Economic Activity around African Protected Areas Captured through Night-Time Light Data
Authors: Anand Anupam , Kim Do-Hyung
Published: January 2021

The importance of tourism for development is widely recognized. Travel restrictions imposed to contain the spread of COVID-19 have brought tourism to a halt. Tourism is one of the key sectors driving change in Africa and is based exclusively on natural assets, with wildlife being the main attraction. Economic activities, therefore, are clustered around conservation and protected areas. We used night-time light data as a proxy measure for economic activity to assess change due to the pandemic. Our analysis shows that overall, 75 percent of the 8427 protected areas saw a decrease in light intensity in varying degrees in all countries and across IUCN protected area categories, including in popular protected area destinations, indicating a reduction in tourism-related economic activities. As countries discuss COVID-19 recovery, the methods using spatially explicit data illustrated in this paper can assess the extent of change, inform decision-making, and prioritize recovery efforts.
Observations of Mesoscale Eddies in Satellite SSS and Inferred Eddy Salt Transport
Authors: Melnichenko Oleg , Hacker Peter , Müller Vasco
Published: January 2021

Observations of sea surface salinity (SSS) from NASA’s Soil Moisture Active-Passive (SMAP) and ESA’s Soil Moisture and Ocean Salinity (SMOS) satellite missions are used to characterize and quantify the contribution of mesoscale eddies to the ocean transport of salt. Given large errors in satellite retrievals and, consequently, SSS maps, we evaluate two products from the two missions and also use two different methods to assess the eddy transport of salt. Comparing the two missions, we find that the estimates of the eddy transport of salt agree very well, particularly in the tropics and subtropics. The transport is divergent in the subtropical gyres (eddies pump salt out of the gyres) and convergent in the tropics. The estimates from the two satellites start to differ regionally at higher latitudes, particularly in the Southern Ocean and along the Antarctic Circumpolar Current (ACC), resulting, presumably, from a considerable increase in the level of noise in satellite retrievals (because of poor sensitivity of the satellite radiometer to SSS in cold water), or they can be due to insufficient spatial resolution. Overall, our study demonstrates that the possibility of characterizing and quantifying the eddy transport of salt in the ocean surface mixed layer can rely on the use of satellite observations of SSS. Yet, new technologies are required to improve the resolution capabilities of future satellite missions in order to observe mesoscale and sub-mesoscale variability, improve the signal-to-noise ratio, and extend these capabilities to the polar oceans.
Impacts of Urbanization on the Muthurajawela Marsh and Negombo Lagoon, Sri Lanka: Implications for Landscape Planning towards a Sustainable Urban Wetland Ecosystem
Authors: Athukorala Darshana , Estoque Ronald C. , Murayama Yuji , Matsushita Bunkei
Published: January 2021

Urban wetland ecosystems (UWEs) play important social and ecological roles but are often adversely affected by urban landscape transformations. Spatio-temporal analyses to gain insights into the trajectories of landscape changes in these ecosystems are needed for better landscape planning towards sustainable UWEs. In this study, we examined the impacts of urbanization on the Muthurajawela Marsh and Negombo Lagoon (MMNL), an important UWE in Sri Lanka that provides valuable ecosystem services. We used remote sensing data to detect changes in the land use/cover (LUC) of the MMNL over a two-decade period (1997–2017) and spatial metrics to characterize changes in landscape composition and configuration. The results revealed that the spatial and socio-economic elements of rapid urbanization of the MMNL had been the main driver of transformation of its natural environment over the past 20 years. This is indicated by a substantial expansion of settlements (+68%) and a considerable decrease of marshland and mangrove cover (−41% and −21%, respectively). A statistical analysis revealed a significant relationship between the change in population density and the loss of wetland due to settlement expansion at the Grama Niladhari division level (n = 99) (where wetland includes marshland, mangrove, and water) (1997–2007: R2 = 0.435, p = 0.000; 2007–2017: R2 = 0.343, p = 0.000). The findings also revealed that most of the observed LUC changes occurred in areas close to roads and growth nodes (viz. Negombo, Ja-Ela, Wattala, and Katana), which resulted in both landscape fragmentation and infill urban expansion. We conclude that, in order to ensure the sustainability of the MMNL, there is an urgent need for forward-looking landscape and urban planning to promote environmentally conscious urban development in the area which is a highly valuable UWE.
Predicting Plant Growth from Time-Series Data Using Deep Learning
Authors: Yasrab Robail , Zhang Jincheng , Smyth Polina , Pound Michael P.
Published: January 2021

Phenotyping involves the quantitative assessment of the anatomical, biochemical, and physiological plant traits. Natural plant growth cycles can be extremely slow, hindering the experimental processes of phenotyping. Deep learning offers a great deal of support for automating and addressing key plant phenotyping research issues. Machine learning-based high-throughput phenotyping is a potential solution to the phenotyping bottleneck, promising to accelerate the experimental cycles within phenomic research. This research presents a study of deep networks’ potential to predict plants’ expected growth, by generating segmentation masks of root and shoot systems into the future. We adapt an existing generative adversarial predictive network into this new domain. The results show an efficient plant leaf and root segmentation network that provides predictive segmentation of what a leaf and root system will look like at a future time, based on time-series data of plant growth. We present benchmark results on two public datasets of Arabidopsis (A. thaliana) and Brassica rapa (Komatsuna) plants. The experimental results show strong performance, and the capability of proposed methods to match expert annotation. The proposed method is highly adaptable, trainable (transfer learning/domain adaptation) on different plant species and mutations.
Hyperspectral Image Classification Based on Multi-Scale Residual Network with Attention Mechanism
Authors: Qing Yuhao , Liu Wenyi
Published: January 2021

In recent years, image classification on hyperspectral imagery utilizing deep learning algorithms has attained good results. Thus, spurred by that finding and to further improve the deep learning classification accuracy, we propose a multi-scale residual convolutional neural network model fused with an efficient channel attention network (MRA-NET) that is appropriate for hyperspectral image classification. The suggested technique comprises a multi-staged architecture, where initially the spectral information of the hyperspectral image is reduced into a two-dimensional tensor, utilizing a principal component analysis (PCA) scheme. Then, the constructed low-dimensional image is input to our proposed ECA-NET deep network, which exploits the advantages of its core components, i.e., multi-scale residual structure and attention mechanisms. We evaluate the performance of the proposed MRA-NET on three public available hyperspectral datasets and demonstrate that, overall, the classification accuracy of our method is 99.82 %, 99.81%, and 99.37, respectively, which is higher compared to the corresponding accuracy of current networks such as 3D convolutional neural network (CNN), three-dimensional residual convolution structure (RES-3D-CNN), and space–spectrum joint deep network (SSRN).
Singular Value Decomposition for Removal of Cardiac Interference from Trunk Electromyogram
Authors: Peri Elisabetta , Xu Lin , Ciccarelli Christian , Vandenbussche Nele L. , Xu Hongji , Long Xi , Overeem Sebastiaan , van Dijk Johannes P. , Mischi Massimo
Published: January 2021

A new algorithm based on singular value decomposition (SVD) to remove cardiac contamination from trunk electromyography (EMG) is proposed. Its performance is compared to currently available algorithms at different signal-to-noise ratios (SNRs). The algorithm is applied on individual channels. An experimental calibration curve to adjust the number of SVD components to the SNR (0–20 dB) is proposed. A synthetic dataset is generated by the combination of electrocardiography (ECG) and EMG to establish a ground truth reference for validation. The performance is compared with state-of-the-art algorithms: gating, high-pass filtering, template subtraction (TS), and independent component analysis (ICA). Its applicability on real data is investigated in an illustrative diaphragm EMG of a patient with sleep apnea. The SVD-based algorithm outperforms existing methods in reconstructing trunk EMG. It is superior to the others in the time (relative mean squared error < 15%) and frequency (shift in mean frequency < 1 Hz) domains. Its feasibility is proven on diaphragm EMG, which shows a better agreement with the respiratory cycle (correlation coefficient = 0.81, p-value < 0.01) compared with TS and ICA. Its application on real data is promising to non-obtrusively estimate respiratory effort for sleep-related breathing disorders. The algorithm is not limited to the need for additional reference ECG, increasing its applicability in clinical practice.
A Novel Method for Determining Angular Speed and Acceleration Using Sin-Cos Encoders
Authors: Alcázar Vargas Manuel , Pérez Fernández Javier , Velasco García Juan M. , Cabrera Carrillo Juan A. , Castillo Aguilar Juan J.
Published: January 2021

The performance of vehicle safety systems depends very much on the accuracy of the signals coming from vehicle sensors. Among them, the wheel speed is of vital importance. This paper describes a new method to obtain the wheel speed by using Sin-Cos encoders. The methodology is based on the use of the Savitzky–Golay filters to optimally determine the coefficients of the polynomials that best fit the measured signals and their time derivatives. The whole process requires a low computational cost, which makes it suitable for real-time applications. This way it is possible to provide the safety system with an accurate measurement of both the angular speed and acceleration of the wheels. The proposed method has been compared to other conventional approaches. The results obtained in simulations and real tests show the superior performance of the proposed method, particularly for medium and low wheel angular speeds.
A Soft Exoskeleton Glove for Hand Bilateral Training via Surface EMG
Authors: Chen Yumiao , Yang Zhongliang , Wen Yangliang
Published: January 2021

Traditional rigid exoskeletons can be challenging to the comfort of wearers and can have large pressure, which can even alter natural hand motion patterns. In this paper, we propose a low-cost soft exoskeleton glove (SExoG) system driven by surface electromyography (sEMG) signals from non-paretic hand for bilateral training. A customization method of geometrical parameters of soft actuators was presented, and their structure was redesigned. Then, the corresponding pressure values of air-pump to generate different angles of actuators were determined to support four hand motions (extension, rest, spherical grip, and fist). A two-step hybrid model combining the neural network and the state exclusion algorithm was proposed to recognize four hand motions via sEMG signals from the healthy limb. Four subjects were recruited to participate in the experiments. The experimental results show that the pressure values for the four hand motions were about −2, 0, 40, and 70 KPa, and the hybrid model can yield a mean accuracy of 98.7% across four hand motions. It can be concluded that the novel SExoG system can mirror the hand motions of non-paretic hand with good performance.
Fault Detection and Identification Method for Quadcopter Based on Airframe Vibration Signals
Authors: Zhang Xiaomin , Zhao Zhiyao , Wang Zhaoyang , Wang Xiaoyi
Published: January 2021

Quadcopters are widely used in a variety of military and civilian mission scenarios. Real-time online detection of the abnormal state of the quadcopter is vital to the safety of aircraft. Existing data-driven fault detection methods generally usually require numerous sensors to collect data. However, quadcopter airframe space is limited. A large number of sensors cannot be loaded, meaning that it is difficult to use additional sensors to capture fault signals for quadcopters. In this paper, without additional sensors, a Fault Detection and Identification (FDI) method for quadcopter blades based on airframe vibration signals is proposed using the airborne acceleration sensor. This method integrates multi-axis data information and effectively detects and identifies quadcopter blade faults through Long and Short-Term Memory (LSTM) network models. Through flight experiments, the quadcopter triaxial accelerometer data are collected for airframe vibration signals at first. Then, the wavelet packet decomposition method is employed to extract data features, and the standard deviations of the wavelet packet coefficients are employed to form the feature vector. Finally, the LSTM-based FDI model is constructed for quadcopter blade FDI. The results show that the method can effectively detect and identify quadcopter blade faults with a better FDI performance and a higher model accuracy compared with the Back Propagation (BP) neural network-based FDI model.
Hyperspectral Imaging (HSI) Technology for the Non-Destructive Freshness Assessment of Pearl Gentian Grouper under Different Storage Conditions
Authors: Chen Zhuoyi , Wang Qingping , Zhang Hui , Nie Pengcheng
Published: January 2021

This study used visible/near-infrared hyperspectral imaging (HSI) technology combined with chemometric methods to assess the freshness of pearl gentian grouper. The partial least square discrimination analysis (PLS-DA) and competitive adaptive reweighted sampling-PLS-DA (CARS-PLS-DA) models were used to classify fresh, refrigerated, and frozen–thawed fish. The PLS-DA model achieved better classification of fresh, refrigerated, and frozen–thawed fish with the accuracy of 100%, 96.43%, and 96.43%, respectively. Further, the PLS regression (PLSR) and CARS-PLS regression (CARS-PLSR) models were used to predict the storage time of fish under different storage conditions, and the prediction accuracy was assessed using the prediction correlation coefficients (Rp2), root mean squared error of prediction (RMSEP), and residual predictive deviation (RPD). For the prediction of storage time, the CARS-PLS model presented the better result of room temperature (Rp2 = 0.948, RMSEP = 0.255, RPD = 4.380) and refrigeration (Rp2 = 0.9319, RMSEP = 1.188, RPD = 3.857), while the better prediction of freeze was by obtained by the PLSR model (Rp2 = 0.9250, RMSEP = 2.910, RPD = 3.469). Finally, the visualization of storage time based on the PLSR model under different storage conditions were realized. This study confirmed the potential of HSI as a rapid and non-invasive technique to identify fish freshness.
Design of a Curved Shape Photonic Crystal Taper for Highly Efficient Mode Coupling
Authors: Jannesari Reyhaneh , Grille Thomas , Consani Cristina , Stocker Gerald , Tortschanoff Andreas , Jakoby Bernhard
Published: January 2021

The design and modeling of a curved shape photonic crystal taper consisting of Si rods integrated with a photonic crystal waveguide are presented. The waveguide is composed of a hexagonal lattice of Si rods and optimized for CO2 sensing based on absorption spectroscopy. We investigated two different approaches to design a taper for a photonic crystal waveguide in a hexagonal lattice of silicon rods. For the first approach (type 1), the taper consists of a square lattice taper followed by a lattice composed of a smooth transition from a square to a hexagonal lattice. In the second approach (type 2), the taper consists of a distorted hexagonal lattice. Different shapes, such as convex, concave, and linear, for the curvature of the taper were considered and investigated. The structure of the taper was improved to enhance the coupling efficiency up to 96% at a short taper length of 25 lattice periods. The finite-difference time-domain (FDTD) technique was used to study the transmission spectrum and the group index. The study proves the improvement of coupling using a curved shape taper. Controlling the group index along the taper could be further improved to enhance the coupling efficiency in a wider spectral range.
Using an Optimization Algorithm to Detect Hidden Waveforms of Signals
Authors: Chang Yen-Ching , Chang Chin-Chen
Published: January 2021

Source signals often contain various hidden waveforms, which further provide precious information. Therefore, detecting and capturing these waveforms is very important. For signal decomposition (SD), discrete Fourier transform (DFT) and empirical mode decomposition (EMD) are two main tools. They both can easily decompose any source signal into different components. DFT is based on Cosine functions; EMD is based on a collection of intrinsic mode functions (IMFs). With the help of Cosine functions and IMFs respectively, DFT and EMD can extract additional information from sensed signals. However, due to a considerably finite frequency resolution, EMD easily causes frequency mixing. Although DFT has a larger frequency resolution than EMD, its resolution is also finite. To effectively detect and capture hidden waveforms, we use an optimization algorithm, differential evolution (DE), to decompose. The technique is called SD by DE (SDDE). In contrast, SDDE has an infinite frequency resolution, and hence it has the opportunity to exactly decompose. Our proposed SDDE approach is the first tool of directly applying an optimization algorithm to signal decomposition in which the main components of source signals can be determined. For source signals from four combinations of three periodic waves, our experimental results in the absence of noise show that the proposed SDDE approach can exactly or almost exactly determine their corresponding separate components. Even in the presence of white noise, our proposed SDDE approach is still able to determine the main components. However, DFT usually generates spurious main components; EMD cannot decompose well and is easily affected by white noise. According to the superior experimental performance, our proposed SDDE approach can be widely used in the future to explore various signals for more valuable information.