bluuebunny/crossref_metadata_2025 huggingface dataset:
Refining the Design of Diblock Elastin-Like Polypeptides for Self-Assembly into Nanoparticles
Authors: Dai Michèle , Georgilis Evangelos , Goudounet Guillaume , Garbay Bertrand , Pille Jan , van Hest Jan C. M. , Schultze Xavier , Garanger Elisabeth , Lecommandoux Sébastien
Published: May 2021

Diblock copolymers based-on elastin-like polypeptide (ELP) have the potential to undergo specific phase transitions when thermally stimulated. This ability is especially suitable to form carriers, micellar structures for instance, for delivering active cargo molecules. Here, we report the design and study of an ELP diblock library based on ELP-[M1V3-i]-[I-j]. First, ELP-[M1V3-i]-[I-j] (i = 20, 40, 60; j = 20, 90) that showed a similar self-assembly propensity (unimer-to-aggregate transition) as their related monoblocks ELP-[M1V3-i] and ELP-[I-j]. By selectively oxidizing methionines of ELP-[M1V3-i] within the different diblocks structures, we have been able to access a thermal phase transition with three distinct regimes (unimers, micelles, aggregates) characteristic of well-defined ELP diblocks.
Enhanced Impact Strength of Recycled PET/Glass Fiber Composites
Authors: Monti Marco , Scrivani Maria , Kociolek Irene , Larsen Åge , Olafsen Kjell , Lambertini Vito
Published: May 2021

In this paper, we report a study on the effects of different ethylene copolymers in improving the impact strength of a fiber-reinforced composite based on a recycled poly(ethylene terephthalate) (rPET) from post-consumer bottles. Different ethylene copolymers have been selected in order to evaluate the effects of the polar co-monomer chemical structure and content. The composite mixtures were prepared via melt extrusion, and the samples were manufactured by injection molding. Impact strength was evaluated using Izod tests, and a morphological study (FESEM) was performed. As a result, a composite with substantially improved impact properties was designed. This study demonstrates that a post-consumer PET from the municipal waste collection of plastic bottles can be successfully used as a matrix of high-performance, injection-molded composites, suitable for use in the automotive sector, among others, with no compromise in terms of mechanical requirements or thermal stability.
Effect of Elevated Temperature on Mechanical Properties of High-Volume Fly Ash-Based Geopolymer Concrete, Mortar and Paste Cured at Room Temperature
Authors: Zhao Jun , Wang Kang , Wang Shuaibin , Wang Zike , Yang Zhaohui , Shumuye Eskinder Desta , Gong Xinglong
Published: May 2021

This paper presents results from experimental work on mechanical properties of geopolymer concrete, mortar and paste prepared using fly ash and blended slag. Compressive strength, splitting tensile strength and flexural strength tests were conducted on large sets of geopolymer and ordinary concrete, mortar and paste after exposure to elevated temperatures. From Thermogravimetric analyzer (TGA), X-ray diffraction (XRD), Scanning electron microscope (SEM) test results, the geopolymer exhibits excellent resistance to elevated temperature. Compressive strengths of C30, C40 and C50 geopolymer concrete, mortar and paste show incremental improvement then followed by a gradual reduction, and finally reach a relatively consistent value with an increase in exposure temperature. The higher slag content in the geopolymer reduces residual strength and the lower exposure temperature corresponding to peak residual strength. Resistance to elevated temperature of C40 geopolymer concrete, mortar and paste is better than that of ordinary concrete, mortar and paste at the same grade. XRD, TGA and SEM analysis suggests that the heat resistance of C–S–H produced using slag is lower than that of sulphoaluminate gel (quartz and mullite, etc.) produced using fly ash. This facilitates degradation of C30, C40 and C50 geopolymer after exposure to elevated temperatures.
Conversion of Electrospun Chitosan into Chitin: A Robust Strategy to Tune the Properties of 2D Biomimetic Nanofiber Scaffolds
Authors: Toncheva-Moncheva Natalia , Aqil Abdelhafid , Galleni Moreno , Jérôme Christine
Published: May 2021

New biomimetic micro- and nano-CsU-based fibrous scaffolds electrospun from solution containing high purity-medical grade chitosan (CsU) of fungus origin (CsU1, Mv ~174,000 and CsU2, 205,000, degree of deacetylation (DDA) ~65%) and polyethylene oxide (PEO, Mv ~ 900,000), in the presence of given amounts of Triton X-100 (from 0.01 to 0.5 wt%) as surfactant were fabricated. We demonstrate that by carefully selecting compositions and surfactant levels, porous mats with CsU content up to 90% (at this molecular weight and DDA) were achieved. Remarkable long-term stability in water or phosphate buffer solution storage were obtained by developing post-electrospinning treatment allowing the complete elimination of the PEO from the CsU-fibers as demonstrated by TGA, DSC and ESEM analysis. Subsequent reacetylation procedure was applied to convert 2D biomimetic chitosan mats to chitin (CsE)-based ones while preserving the nanofiber structure. This innovative procedure allows tuning and modifying the thermal, mechanical properties and more importantly the biodegradation abilities (fast enzymatic biodegradation in some cases and slower on the others) of the prepared nanofibrous mats. The established reproducible method offers the unique advantage to modulate the membrane properties leading to stable 2D biomimetic CsU and/or chitin (CsE) scaffolds tailor-made for specific purposes in the field of tissue engineering.
Strategies towards Reduction of Cellulases Consumption: Debottlenecking the Economics of Lignocellulosics Valorization Processes
Authors: Gomes Daniel , Cunha Joana , Zanuso Elisa , Teixeira José , Domingues Lucília
Published: May 2021

Lignocellulosic residues have been receiving growing interest as a promising source of polysaccharides, which can be converted into a variety of compounds, ranging from biofuels to bioplastics. Most of these can replace equivalent products traditionally originated from petroleum, hence representing an important environmental advantage. Lignocellulosic materials are theoretically unlimited, cheaper and may not compete with food crops. However, the conversion of these materials to simpler sugars usually requires cellulolytic enzymes. Being still associated with a high cost of production, cellulases are commonly considered as one of the main obstacles in the economic valorization of lignocellulosics. This work provides a brief overview of some of the most studied strategies that can allow an important reduction of cellulases consumption, hence improving the economy of lignocellulosics conversion. Cellulases recycling is initially discussed regarding the main processes to recover active enzymes and the most important factors that may affect enzyme recyclability. Similarly, the potential of enzyme immobilization is analyzed with a special focus on the contributions that some elements of the process can offer for prolonged times of operation and improved enzyme stability and robustness. Finally, the emergent concept of consolidated bioprocessing (CBP) is also described in the particular context of a potential reduction of cellulases consumption.
Multi-Objective Dispatching Optimization of an Island Microgrid Integrated with Desalination Units and Electric Vehicles
Authors: Zhu Wenqiang , Guo Jiang , Zhao Guo
Published: May 2021

The renewable energy microgrid is an effective solution for island energy supply with the advantages of low energy cost, environmental protection, and reliability. In this paper, an island renewable energy microgrid integrated with desalination units and electric vehicles is established to meet the self-satisfaction of the island’s sustainable electricity, fresh water, and transportation. The source side components of the system include photovoltaic cells, wind turbines, diesel generators, battery energy storage systems. A multi-objective dispatching optimization method based on the flexibility of electric vehicles and desalination units is proposed comprehensively considering the economy and renewable energy penetration indexes. The optimization objectives are minimizing the comprehensive operating cost, and the net load fluctuation. An improved multi-objective grey wolf optimizer is adopted to solve the dispatching problem. The system is modeled and simulated by MATLAB software. The feasibility of the proposed dispatching optimization method is verified by case studies and operation simulation. Four different cases are compared and analyzed to study the impact of EVs and DES on dispatching optimization.
Impact of Indigenous Non-Saccharomyces Yeasts Isolated from Madeira Island Vineyards on the Formation of Ethyl Carbamate in the Aging of Fortified Wines
Authors: Leça João Micael , Pereira Vanda , Miranda Andreia , Vilchez José Luis , Malfeito-Ferreira Manuel , Marques José Carlos
Published: May 2021

The impact of selected non-Saccharomyces yeasts on the occurrence of ethyl carbamate (EC) was evaluated. Hanseniaspora uvarum, Starmerella bacillaris, Pichia terricola, Pichia fermentans and Pichia kluyveri isolated from Madeira Island vineyards were inoculated in Tinta Negra musts. Urea, citrulline (Cit) and arginine (Arg) were quantified when the density of musts attained the levels to obtain sweet (1052 ± 5 g/L) and dry (1022 ± 4 g/L) Madeira wines. The urea concentration varied between 1.3 and 5.3 mg/L, Cit from 10.6 to 15.1 mg/L and Arg between 687 and 959 mg/L. P. terricola and S. bacillaris generated lower levels of urea (<2.5 mg/L), Cit (<11.0 mg/L) and Arg (<845.6 mg/L). The five resulting fortified wines, individually fermented by the selected non-Saccharomyces yeast, were exposed to laboratory-accelerated aging at 70 °C for 1 month. From the studied yeasts, P. terricola and S. bacillaris revealed a lower potential to form EC (<100 µg/L); therefore, both yeasts can be a useful tool for its mitigation in wines.
Scaling-Up and Long-Term Operation of a Full-Scale Two-Stage Partial Nitritation-Anammox System Treating Landfill Leachate
Authors: Magrí Albert , Ruscalleda Maël , Vilà Albert , Akaboci Tiago R. V. , Balaguer M. Dolors , Llenas Josep M. , Colprim Jesús
Published: May 2021

(1) Background: Biological treatment of leachate in landfill sites using anaerobic ammonium oxidation (anammox) is challenging because of the intrinsic characteristics of this complex wastewater. In this work, the scale-up and subsequent full-scale implementation of the PANAMMOX® technology (LEQUIA Research Group, Girona, Catalonia, Spain) are presented as a case study to achieve long-term nitrogen (N) removal from mature leachate mostly through a completely autotrophic pathway. (2) Methods: The treatment system consists of two sequencing batch reactors (SBRs) running in series to individually operate partial nitritation (PN) and anammox (A). Following biological treatment, physicochemical oxidation (i.e., Fenton-based process) was used to remove the remaining non-biodegradable organic matter. A cost analysis comparative was conducted in relation to the former technology used on-site for treating the leachate. (3) Results: The scale-up of the process from pilot- to full-scale was successfully achieved, finally reaching an average removal of 7.4 kg N/d. The composition of the leachate changed over time, but especially once the landfill site stopped receiving solid waste (this fact involved a marked increase in the strength of the leachate). The adjustment of the alkalinity-to-ammonium ratio before feeding PN-SBR helped to improve the N-removal efficiency. Values of conductivity above 25 mS/cm in A-SBR could negatively affect the performance of the anammox process, making it necessary to consider a dilution strategy according to the on-line monitoring of this parameter. The analysis of the operational costs showed that by implementing the PANAMMOX® technology (LEQUIA Research Group, Girona, Catalonia, Spain) in the landfill site, savings up to 32% were achievable. (4) Conclusions: Treatment of mature landfill leachate in such a two-stage PN-A system was demonstrated as feasible and economically appealing despite the complexity of this industrial wastewater. Accurate expert supervision of the process was a key factor to reaching good performances.
Dendranthema zawadskii var. lucidum (Nakai) J.H. Park Extract Inhibits Cellular Senescence in Human Dermal Fibroblasts and Aging-Related Inflammation in Rats
Authors: Choi Jehun , Jang Gwi-Yeong , Lee Jeonghoon , Chung Hae-Young , Noh Hyung-Jun , Kim Geum-Soog , Lee Seung-Eun
Published: May 2021

Senescence is the phenomenon by which physiological functions of organisms degenerate with time. Cellular senescence is marked by an inhibition of cell cycle progression. Beta-galactosidase accumulates in the lysosomes of aged cells. In this study, human dermal fibroblast cells (HDFs) were treated with 0.5 μM doxorubicin for 4 h to induce cellular senescence. Senescence-associated beta-galactosidase (SA-β-gal) activity was then measured 72 h after treatment with aerial parts of Dendranthema zawadskii var. lucidum (Nakai) J.H. Park (DZ) extract. Treatment with DZ extract significantly decreased SA-β-gal activity in a dose-dependent manner in HDFs. Additionally, DZ extract treatment reduced age-related oxidative stress and inflammation in the aortas of aged rats. The reactive oxygen species (ROS) levels in aortas of aged control rats were higher than those in young rats. However, DZ extract-fed aged rats showed significantly lower ROS levels than the aged control rats. When the aged rats were treated with DZ extract at either 0.2 or 1.0 mg∙kg−1∙day−1, NF-κB levels in aorta tissue decreased significantly compared to those in aorta tissue of the aged control rats without DZ treatment. In addition, DZ extract-fed aged rat aortas showed significant reductions in expression of iNOS and COX-2 induced by NF-κB translocation. Therefore, these results suggest that DZ effectively inhibited senescence-related NF-κB activation and inflammation. DZ extract may have a role in the prevention of the vascular inflammatory responses that occur during vascular aging.
Survey of Current Predoctoral Removable Partial Denture Curriculum in the United States
Authors: Kim Jiyeon J. , Yuan Judy Chia-Chun , Sukotjo Cortino , Campbell Stephen D.
Published: May 2021

The purpose of this survey was to determine removable partial denture (RPD) framework design concepts, new materials, and digital technology that are currently being taught in the predoctoral RPD curriculums in the U.S. dental schools. A questionnaire including RPD framework design concepts, materials, and digital technology was created on Qualtrics. The link to the questionnaire was distributed by email in May 2018 to U.S. dental schools. Thirty-nine of the sixty-six schools responded, yielding a response rate of 59%. Most schools are utilizing textbooks by McCracken and/or Stewart as their primary reference (90%). Only a few schools teach incisal rest (24%) and intra-coronal retainers (8%) in the clinic. The majority of schools teach three or less clasp assembly variations for distal extension RPD scenarios and utilize altered cast impression techniques only for selective patient scenarios. Half of schools currently utilize digital technology in their RPD curriculum. Although twenty-five schools (69%) indicated that implant-assisted RPD (IARPD) is being taught in the curriculum, only nineteen of them teach IARPDs in the clinic. Half of schools are utilizing non-metal clasp denture (NMCD) in their curriculum; most of these school provide this treatment as immediate/interim partials or long-term interim partials. Many U.S. dental schools are simplifying RPD design concepts and principles that are being taught. More schools are introducing new concepts and materials such as IARPD and NMCDs while incorporating digital technology in their predoctoral RPD curriculum.
Mental Health Literacy and Dementia
Authors: Carr Hannah , Furnham Adrian
Published: May 2021

This study aimed to investigate mental health literacy (MHL) with respect to dementia. Three forms of dementia were investigated. In all, 167 participants completed an online questionnaire which consisted of five vignettes that described the three dementia conditions, as well as depression and typical ageing. The vignette characters had no age specified, or they were described as 50-years-old or 70-years-old. Participants had to firstly decide if there was a disorder present and identify it by name, then answer questions relating to treatment and help-seeking. Results showed that participants could identify Alzheimer’s Disease significantly more so than they could vascular or frontotemporal dementia. All three dementias were significantly more recognised when the vignette was described as a 70-year-old. Frontotemporal dementia was significantly misdiagnosed as depression. Participant education and mental health experience did not influence the identification of dementia. Compared to some other well-known mental illnesses like schizophrenia, lay people are relatively good at recognising Alzheimer’s disease, but much less so at other forms of dementia. Implications and limitations of the study are discussed.
Moral Aspects of Imaginative Art in Thomas Aquinas
Authors: Roszak Piotr , Berry John Anthony
Published: May 2021

For Thomas Aquinas, the imagination, being one of the “inner senses”, is a doorway to attain true knowledge. In this paper, we first analyze his lexicon in this regard (imaginatio and phantasia). Second, we discuss imagination as the subject matter of the intellectual virtues, which facilitate cognition and judgment. The development of imagination is the foundation of his vision of education not only on the natural but also on the supernatural level. Third, we explore Aquinas’ moral assessment of imaginative art and finally its influence on shaping the character. This influence occurs on two levels: it is assessed from the perspective of charity, justice, prudence and purity, namely to what extent the art serves these values, whereas the second criterion is beauty.
The Persistence of Sacrifice as Self-Sacrifice and Its Contemporary Embodiment in the 9/11 Rescuers and COVID-19 Healthcare Professionals
Authors: Gil-Gimeno Javier , Sánchez Capdequí Celso
Published: May 2021

The aim of this paper is to analyze the persistence of sacrifice as self-sacrifice in contemporary societies. In order to reach this goal, firstly, we discuss how in the Axial Age (800–200 B.C.E.) an understanding of sacrifice as ritual worship or a ritual practice that involves the immolation of a victim became less prevalent and a new understanding of sacrifice emerges. This new notion of sacrifice focuses on individual relinquishment and gift exchange, that is, on a person sacrificing or relinquishing him/herself as a gift that is given in an exchange relationship for protecting a greater good (a god, a community, a person, a nation, and so on). Secondly, we analyze how this new sacrifice formula had an important impact on the understanding of sacrifice. Most notably, it led people to conceptualize sacrifice as a project or as something that persons could intentionally embrace. Thirdly, and as a result of the previous processes, we attend to the secularization of sacrifice, not in the sense of a de-sacralization of this phenomenon but in the way of sacralization of the mundane realm and mundane things, such as intentional self-sacrificial acts, in social contexts where there is religious pluralism. Insight into how the notion of sacrifice is secularized is found throughout the classic works of Marcel Mauss and Georg Simmel, and these works are discussed in section three. Fourthly, we study the sacredness of the person as a clear type of secular religiosity that develops self-sacrificial forms. Two of these self-sacrificial forms are the actions of 9/11 rescuers and COVID-19 healthcare professionals. A short analysis of both will serve us to illustrate how self-sacrifice is embodied in contemporary societies.
Exchange Rate Volatility, Currency Misalignment, and Risk of Recession in the Central and Eastern European Countries
Authors: Shevchuk Victor , Kopych Roman
Published: May 2021

This study is aimed at estimation of the exchange rate volatility and its impact on the business cycle fluctuations in four central and eastern European countries (the Czech Republic, Hungary, Poland, and Romania). Exchange rate volatility is estimated with the EGARCH(1,1) model. It is found that exchange rate volatility is affected by the components of the Index of Economic Freedom from the Heritage Foundation, besides inflation and crisis developments. The empirical results using GMM estimation technique and comprehensive robustness checks suggest that exchange rate volatility reduces the risk of recession in the Czech Republic while the opposite effect is found for Hungary and Romania, with a neutrality for Poland. These findings continue to hold after controlling for the fiscal and monetary policy indicators. There is evidence that the RER undervaluation prevents sliding into a recession on a credible basis in Poland only, with a neutral stance for other countries. Except in Romania, higher levels of economic freedom is associated with worsening of the cyclical position of output. Among other results, stabilization policies in the recession imply fiscal tightening for the Czech Republic and Romania, higher money supply for the Czech Republic and Poland, and lower central bank reference rate for Hungary.
Liability for Incorrect Client Personalization in the Distribution of Consumer Insurance
Authors: Tereszkiewicz Piotr , Południak-Gierz Katarzyna
Published: May 2021

The use of personalization mechanisms should allow the insurance distributor to reduce exploration costs and adjust the offered insurance product to the needs, features, and situation of each individual client. This study seeks to examine how liability should be allocated when the process of the personalization of an insurance product does not result in the client’s choice of an optimal product. First, we identify the typical uses of new technologies allowing for an adjustment of insurance contracts. Second, we analyze the interplay between their application and the legal obligations of insurance product distributors. Subsequently, the paper discusses the scope of factors the insurance distributor is liable for when using personalizing tools in contacts with clients. We submit that offering an online personalization of insurance products ought to be regarded as being equivalent to providing advice under Art. 2, Sec. 1, Point 15 of the European Union Insurance Distribution Directive (IDD). From the consumer’s perspective, our analysis makes the case for the insurance distributor’s liability for mispersonalization of an insurance contract.
Risk Approach—Risk Hierarchy or Construction Investment Risks in the Light of Interim Empiric Primary Research Conclusions
Authors: Szemere Tibor Pál , Garai-Fodor Mónika , Csiszárik-Kocsir Ágnes
Published: May 2021

The focus of this study is to examine the investment project process. Since investment can also be considered as economic interactions, certain risks are associated with their implementation. Risk factors were given a particular priority during the secondary and primary research, while determining the most relevant risk factors of investment project processes in relation to the B2B market. The risk map for investment project processes was created in line with the relevant secondary sources, qualitative and quantitative primary results. This is topical because the importance of investments is unquestionable in a market economy. Therefore, a comprehensive risk assessment might provide results that are useful for both supply and demand side actors in B2B market relations. Based on the results of the primary study, the perceived risks of the project process were defined, and they were structured into a risk hierarchy system. Based on the qualitative results, we performed a quantitative study. Based on the responses of the sample subjects, we determined the perceived risk factors, and on the basis of them, we segmented the service provider (contractor) market. The main socio-demographic characteristics of each segment were also explored in the framework of the research.
The Use of Virtual Reality to Promote Sustainable Tourism: A Case Study of Wooden Churches Historical Monuments from Romania
Authors: Caciora Tudor , Herman Grigore Vasile , Ilieș Alexandru , Baias Ștefan , Ilieș Dorina Camelia , Josan Ioana , Hodor Nicolaie
Published: May 2021

The accelerated development and expansion of cultural tourism in areas with unique tourist objectives, characterised by a high degree of risk in terms of their physical and chemical integrity, requires sustained efforts by all stakeholders to identify new methods, techniques, and procedures for their conservation, protection, and capitalisation, with respect to tourism. The aim of this study was to propose an optimal methodology for capitalising on tourism related to wooden churches, regarded as a structural item of tangible cultural heritage, with positive effects on the protection, conservation, information, and awareness of all stakeholders in tourism development. This involved the development of a web portal, in which were integrated the 3D models related to the analysed objects, the panoramic images inside them, the audio support, the photographs, and the accompanying text necessary to create and render a virtual reality (VR) production for purposes of virtual tourism (VT). The results obtained consisted of the creation of the website Bihor360°, which is freely navigable and whose content, including both textual and graphic information, can be easily accessed by all interested users. The aim involved was to release an online bridge for potential visitors to the fragile tourist attractions, facilitating the development of active knowledge and VR while raising awareness among the population and the local authorities about the role and the importance of the wooden churches in tourism and the local economy.
Archaeologic Machine Learning for Shipwreck Detection Using Lidar and Sonar
Authors: Character Leila , Ortiz JR Agustin , Beach Tim , Luzzadder-Beach Sheryl
Published: May 2021

The objective of this project is to create a new implementation of a deep learning model that uses digital elevation data to detect shipwrecks automatically and rapidly over a large geographic area. This work is intended to apply a new methodology to the field of underwater archaeology. Shipwrecks represent a major resource to understand maritime human activity over millennia, but underwater archaeology is expensive, misappropriated, and hazardous. An automated tool to rapidly detect and map shipwrecks can therefore be used to create more accurate maps of natural and archaeological features to aid management objectives, study patterns across the landscape, and find new features. Additionally, more comprehensive and accurate shipwreck maps can help to prioritize site selection and plan excavation. The model is based on open source topo-bathymetric data and shipwreck data for the United States available from NOAA. The model uses transfer learning to compensate for a relatively small sample size and addresses a recurring problem that associated work has had with false positives by training the model both on shipwrecks and background topography. Results of statistical analyses conducted—ANOVAs and box and whisker plots—indicate that there are substantial differences between the morphologic characteristics that define shipwrecks vs. background topography, supporting this approach to addressing false positives. The model uses a YOLOv3 architecture and produced an F1 score of 0.92 and a precision score of 0.90, indicating that the approach taken herein to address false positives was successful.
Deep Convolutional Denoising Autoencoders with Network Structure Optimization for the High-Fidelity Attenuation of Random GPR Noise
Authors: Feng Deshan , Wang Xiangyu , Wang Xun , Ding Siyuan , Zhang Hua
Published: May 2021

The high-fidelity attenuation of random ground penetrating radar (GPR) noise is important for enhancing the signal-noise ratio (SNR). In this paper, a novel network structure for convolutional denoising autoencoders (CDAEs) was proposed to effectively resolve various problems in the noise attenuation process, including overfitting, the size of the local receptive field, and representational bottlenecks and vanishing gradients in deep learning; this approach also significantly improves the noise attenuation performance. We described the noise attenuation process of conventional CDAEs, and then presented the output feature map of each convolutional layer to analyze the role of convolutional layers and their impacts on GPR data. Furthermore, we focused on the problems of overfitting, the local receptive field size, and the occurrence of representational bottlenecks and vanishing gradients in deep learning. Subsequently, a network structure optimization strategy, including a dropout regularization layer, an atrous convolution layer, and a residual-connection structure, was proposed, namely convolutional denoising autoencoders with network structure optimization (CDAEsNSO), comprising an intermediate version, called atrous-dropout CDAEs (AD-CDAEs), and a final version, called residual-connection CDAEs (ResCDAEs), all of which effectively improve the performance of conventional CDAEs. Finally, CDAEsNSO was applied to attenuate noise for the H-beam model, tunnel lining model, and field pipeline data, confirming that the algorithm adapts well to both synthetic and field data. The experiments verified that CDAEsNSO not only effectively attenuates strong Gaussian noise, Gaussian spike impulse noise, and mixed noise, but it also causes less damage to the original waveform data and maintains high-fidelity information.
Proximal Imaging of Changes in Photochemical Reflectance Index in Leaves Based on Using Pulses of Green-Yellow Light
Authors: Sukhov Vladimir , Sukhova Ekaterina , Khlopkov Andrey , Yudina Lyubov , Ryabkova Anastasiia , Telnykh Alexander , Sergeeva Ekaterina , Vodeneev Vladimir , Turchin Ilya
Published: May 2021

Plants are affected by numerous environmental factors that influence their physiological processes and productivity. Early revealing of their action based on measuring spectra of reflected light and calculating reflectance indices is an important stage in the protection of agricultural plants. Photochemical reflectance index (PRI) is a widely used parameter related to photosynthetic changes in plants under action of stressors. We developed a new system for proximal imaging of PRI based on using short pulses of measuring light detected simultaneously in green (530 nm) and yellow (570 nm) spectral bands. The system has several advances compared to those reported in literature. Active light illumination and subtraction of the ambient light allow for PRI measurements without periodic calibrations. Short duration of measuring pulses (18 ms) minimizes their influence on plants. Measurements in two spectral bands operated by separate cameras with aligned fields of visualization allow one to exclude mechanically switchable parts like filter wheels thus minimizing acquisition time and increasing durability of the setup. Absolute values of PRI and light-induced changes in PRI (ΔPRI) in pea leaves and changes of these parameters under action of light with different intensities, water shortage, and heating have been investigated using the developed setup. Changes in ΔPRI are shown to be more robust than the changes in the absolute value of PRI which is in a good agreement with our previous studies. Values of PRI and, especially, ΔPRI are strongly linearly related to the energy-dependent component of the non-photochemical quenching and can be potentially used for estimation of this component. Additionally, we demonstrate that the developed system can also measure fast changes in PRI (hundreds of milliseconds and seconds) under leaf illumination by the pulsed green-yellow measuring light. Thus, the developed system of proximal PRI imaging can be used for PRI measurements (including fast changes in PRI) and estimation of stressors-induced photosynthetic changes.
Understanding Growth Dynamics and Yield Prediction of Sorghum Using High Temporal Resolution UAV Imagery Time Series and Machine Learning
Authors: Varela Sebastian , Pederson Taylor , Bernacchi Carl J. , Leakey Andrew D. B.
Published: May 2021

Unmanned aerial vehicles (UAV) carrying multispectral cameras are increasingly being used for high-throughput phenotyping (HTP) of above-ground traits of crops to study genetic diversity, resource use efficiency and responses to abiotic or biotic stresses. There is significant unexplored potential for repeated data collection through a field season to reveal information on the rates of growth and provide predictions of the final yield. Generating such information early in the season would create opportunities for more efficient in-depth phenotyping and germplasm selection. This study tested the use of high-resolution time-series imagery (5 or 10 sampling dates) to understand the relationships between growth dynamics, temporal resolution and end-of-season above-ground biomass (AGB) in 869 diverse accessions of highly productive (mean AGB = 23.4 Mg/Ha), photoperiod sensitive sorghum. Canopy surface height (CSM), ground cover (GC), and five common spectral indices were considered as features of the crop phenotype. Spline curve fitting was used to integrate data from single flights into continuous time courses. Random Forest was used to predict end-of-season AGB from aerial imagery, and to identify the most informative variables driving predictions. Improved prediction of end-of-season AGB (RMSE reduction of 0.24 Mg/Ha) was achieved earlier in the growing season (10 to 20 days) by leveraging early- and mid-season measurement of the rate of change of geometric and spectral features. Early in the season, dynamic traits describing the rates of change of CSM and GC predicted end-of-season AGB best. Late in the season, CSM on a given date was the most influential predictor of end-of-season AGB. The power to predict end-of-season AGB was greatest at 50 days after planting, accounting for 63% of variance across this very diverse germplasm collection with modest error (RMSE 1.8 Mg/ha). End-of-season AGB could be predicted equally well when spline fitting was performed on data collected from five flights versus 10 flights over the growing season. This demonstrates a more valuable and efficient approach to using UAVs for HTP, while also proposing strategies to add further value.
Unsupervised Learning of Depth from Monocular Videos Using 3D-2D Corresponding Constraints
Authors: Jin Fusheng , Zhao Yu , Wan Chuanbing , Yuan Ye , Wang Shuliang
Published: May 2021

Depth estimation can provide tremendous help for object detection, localization, path planning, etc. However, the existing methods based on deep learning have high requirements on computing power and often cannot be directly applied to autonomous moving platforms (AMP). Fifth-generation (5G) mobile and wireless communication systems have attracted the attention of researchers because it provides the network foundation for cloud computing and edge computing, which makes it possible to utilize deep learning method on AMP. This paper proposes a depth prediction method for AMP based on unsupervised learning, which can learn from video sequences and simultaneously estimate the depth structure of the scene and the ego-motion. Compared with the existing unsupervised learning methods, our method makes the spatial correspondence among pixel points consistent with the image area by smoothing the 3D corresponding vector field based on 2D image, which effectively improves the depth prediction ability of the neural network. Our experiments on the KITTI driving dataset demonstrated that our method outperformed other previous learning-based methods. The results on the Apolloscape and Cityscapes datasets show that our proposed method has a strong universality.
Earth’s Time-Variable Gravity from GRACE Follow-On K-Band Range-Rates and Pseudo-Observed Orbits
Authors: Koch Igor , Duwe Mathias , Flury Jakob , Shabanloui Akbar
Published: May 2021

During its science phase from 2002–2017, the low-low satellite-to-satellite tracking mission Gravity Field Recovery And Climate Experiment (GRACE) provided an insight into Earth’s time-variable gravity (TVG). The unprecedented quality of gravity field solutions from GRACE sensor data improved the understanding of mass changes in Earth’s system considerably. Monthly gravity field solutions as the main products of the GRACE mission, published by several analysis centers (ACs) from Europe, USA and China, became indispensable products for quantifying terrestrial water storage, ice sheet mass balance and sea level change. The successor mission GRACE Follow-On (GRACE-FO) was launched in May 2018 and proceeds observing Earth’s TVG. The Institute of Geodesy (IfE) at Leibniz University Hannover (LUH) is one of the most recent ACs. The purpose of this article is to give a detailed insight into the gravity field recovery processing strategy applied at LUH; to compare the obtained gravity field results to the gravity field solutions of other established ACs; and to compare the GRACE-FO performance to that of the preceding GRACE mission in terms of post-fit residuals. We use the in-house-developed MATLAB-based GRACE-SIGMA software to compute unconstrained solutions based on the generalized orbit determination of 3 h arcs. K-band range-rates (KBRR) and kinematic orbits are used as (pseudo)-observations. A comparison of the obtained solutions to the results of the GRACE-FO Science Data System (SDS) and Combination Service for Time-variable Gravity Fields (COST-G) ACs, reveals a competitive quality of our solutions. While the spectral and spatial noise levels slightly differ, the signal content of the solutions is similar among all ACs. The carried out comparison of GRACE and GRACE-FO KBRR post-fit residuals highlights an improvement of the GRACE-FO K-band ranging system performance. The overall amplitude of GRACE-FO post-fit residuals is about three times smaller, compared to GRACE. GRACE-FO post-fit residuals show less systematics, compared to GRACE. Nevertheless, the power spectral density of GRACE-FO and GRACE post-fit residuals is dominated by similar spikes located at multiples of the orbital and daily frequencies. To our knowledge, the detailed origin of these spikes and their influence on the gravity field recovery quality were not addressed in any study so far and therefore deserve further attention in the future. Presented results are based on 29 monthly gravity field solutions from June 2018 until December 2020. The regularly updated LUH-GRACE-FO-2020 time series of monthly gravity field solutions can be found on the website of the International Centre for Global Earth Models (ICGEM) and in LUH’s research data repository. These operationally published products complement the time series of the already established ACs and allow for a continuous and independent assessment of mass changes in Earth’s system.
Concurrent Firing Light Detection and Ranging System for Autonomous Vehicles
Authors: Kim Gunzung , Ashraf Imran , Eom Jeongsook , Park Yongwan
Published: May 2021

We proposed a light detection and ranging (LIDAR) system that changes the measurement strategy from a LIDAR system of sequential emission and measuring method to a concurrent firing measuring method. The proposed LIDAR was a 3D scanning LIDAR method that consisted of 128 output channels in one vertical line in the measurement direction and concurrently measured the distance for each of these 128 channels. The scanning LIDAR emitted 128 laser pulse streams encoded by carrier-hopping prime code (CHPC) technology with identification and checksum. When the reflected pulse stream was received and demodulated, the emission channel could be recognized. This information could be used to estimate the time when the laser pulse stream was emitted and calculate the distance to the object reflecting the laser. By using the identification of the received reflected wave, even if several positions were measured at the same time, the measurement position could be recognized after the reception. Extensive simulations indicated that the proposed LIDAR could provide autonomous vehicles or autonomous walking robots with good distance images to recognize the environment ahead.
A Scalable Machine Learning Pipeline for Paddy Rice Classification Using Multi-Temporal Sentinel Data
Authors: Sitokonstantinou Vasileios , Koukos Alkiviadis , Drivas Thanassis , Kontoes Charalampos , Papoutsis Ioannis , Karathanassi Vassilia
Published: May 2021

The demand for rice production in Asia is expected to increase by 70% in the next 30 years, which makes evident the need for a balanced productivity and effective food security management at a national and continental level. Consequently, the timely and accurate mapping of paddy rice extent and its productivity assessment is of utmost significance. In turn, this requires continuous area monitoring and large scale mapping, at the parcel level, through the processing of big satellite data of high spatial resolution. This work designs and implements a paddy rice mapping pipeline in South Korea that is based on a time-series of Sentinel-1 and Sentinel-2 data for the year of 2018. There are two challenges that we address; the first one is the ability of our model to manage big satellite data and scale for a nationwide application. The second one is the algorithm’s capacity to cope with scarce labeled data to train supervised machine learning algorithms. Specifically, we implement an approach that combines unsupervised and supervised learning. First, we generate pseudo-labels for rice classification from a single site (Seosan-Dangjin) by using a dynamic k-means clustering approach. The pseudo-labels are then used to train a Random Forest (RF) classifier that is fine-tuned to generalize in two other sites (Haenam and Cheorwon). The optimized model was then tested against 40 labeled plots, evenly distributed across the country. The paddy rice mapping pipeline is scalable as it has been deployed in a High Performance Data Analytics (HPDA) environment using distributed implementations for both k-means and RF classifiers. When tested across the country, our model provided an overall accuracy of 96.69% and a kappa coefficient 0.87. Even more, the accurate paddy rice area mapping was returned early in the year (late July), which is key for timely decision-making. Finally, the performance of the generalized paddy rice classification model, when applied in the sites of Haenam and Cheorwon, was compared to the performance of two equivalent models that were trained with locally sampled labels. The results were comparable and highlighted the success of the model’s generalization and its applicability to other regions.
Effect of Different Atmospheric Correction Algorithms on Sentinel-2 Imagery Classification Accuracy in a Semiarid Mediterranean Area
Authors: Valdivieso-Ros Carmen , Alonso-Sarria Francisco , Gomariz-Castillo Francisco
Published: May 2021

Multi-temporal imagery classification using spectral information and indices with random forest allows improving accuracy in land use and cover classification in semiarid Mediterranean areas, where the high fragmentation of the landscape caused by multiple factors complicates the task. Hence, since data come from different dates, atmospheric correction is needed to retrieve surface reflectivity values. The Sen2Cor, MAJA and ACOLITE algorithms have proven their good performances in these areas in different comparative studies, and DOS is a basic method that is widely used. The aim in this study was to test the feasibility of its application to the data set to improve the values of accuracy in classification and the performance in properly labelling different classes. Additionally, we tried to correct accuracy and separability mixing predictors with different algorithms. The results showed that, using a single algorithm, the general accuracy and kappa index from ACOLITE were the highest, 0.80 ± 0.01 and 0.76 ± 0.01., but the separability between problematic classes was slightly improved by using MAJA. Any combination of the different algorithms tested increased the values of classification, although they may help with separability between some pairs of classes.
Editorial for Special Issue “Monitoring Land Subsidence Using Remote Sensing”
Authors: Fabris Massimo , Cenni Nicola , Fiaschi Simone
Published: May 2021

Land subsidence is a geological hazard that affects several different communities around the world [...]
Self-Matching CAM: A Novel Accurate Visual Explanation of CNNs for SAR Image Interpretation
Authors: Feng Zhenpeng , Zhu Mingzhe , Stanković Ljubiša , Ji Hongbing
Published: May 2021

Synthetic aperture radar (SAR) image interpretation has long been an important but challenging task in SAR imaging processing. Generally, SAR image interpretation comprises complex procedures including filtering, feature extraction, image segmentation, and target recognition, which greatly reduce the efficiency of data processing. In an era of deep learning, numerous automatic target recognition methods have been proposed based on convolutional neural networks (CNNs) due to their strong capabilities for data abstraction and mining. In contrast to general methods, CNNs own an end-to-end structure where complex data preprocessing is not needed, thus the efficiency can be improved dramatically once a CNN is well trained. However, the recognition mechanism of a CNN is unclear, which hinders its application in many scenarios. In this paper, Self-Matching class activation mapping (CAM) is proposed to visualize what a CNN learns from SAR images to make a decision. Self-Matching CAM assigns a pixel-wise weight matrix to feature maps of different channels by matching them with the input SAR image. By using Self-Matching CAM, the detailed information of the target can be well preserved in an accurate visual explanation heatmap of a CNN for SAR image interpretation. Numerous experiments on a benchmark dataset (MSTAR) verify the validity of Self-Matching CAM.
An Enhanced Data-Driven Array Shape Estimation Method Using Passive Underwater Acoustic Data
Authors: Wu Qisong , Zhang Hao , Lai Zhichao , Xu Youhai , Yao Shuai , Tao Jun
Published: May 2021

Beamforming-based signal enhancement technologies in passive sonar array processing often have poor performance due to array distortion caused by rapid tactical maneuvers of the towed platform, oceanic currents, hydrodynamic effects, etc. In this paper, an enhanced data-driven shape array estimation formulation is proposed using passive underwater acoustic data. Beamforming based on a hypothetically ideal array is firstly employed to perform the detection of narrow-band components from sources of opportunity, and the corresponding phases of these detected narrow-band components are subsequently extracted to acquire time-delay differences. Then, a weighted outlier-robust Kalman smoother is proposed to acquire enhanced estimates of the time-delay differences, since the underlying properties of slowly changing time-delay differences in the hydrophone array and diverse signal to interference and noise ratios in multiple narrow-band components are explored; and its Cramer–Rao Lower Bound is also provided. Finally, the hydrophone array shape is estimated based on the estimated time delay differences. The proposed formulation fully exploits directional radiated noise signals from distant underwater acoustic targets as sources of opportunity for real-time array shape estimation, and thus it requires neither the number nor direction of sources to be known in advance. The effectiveness of the proposed method is validated in simulations and real experimental data.