bluuebunny/crossref_metadata_2025 huggingface dataset:
Efficacy of Ursolic Acid-Enriched Water-Soluble and Not Cytotoxic Nanoparticles against Enterococci
Authors: Schito Anna Maria , Caviglia Debora , Piatti Gabriella , Zorzoli Alessia , Marimpietri Danilo , Zuccari Guendalina , Schito Gian Carlo , Alfei Silvana
Published: November 2021

Ursolic acid (UA), a pentacyclic triterpenoid acid found in many medicinal plants and aromas, is known for its antibacterial effects against multi-drug-resistant (MDR) Gram-positive bacteria, which seriously threaten human health. Unfortunately, UA water-insolubility, low bioavailability, and systemic toxicity limit the possibilities of its application in vivo. Consequently, the beneficial activities of UA observed in vitro lose their potential clinical relevance unless water-soluble, not cytotoxic UA formulations are developed. With a nano-technologic approach, we have recently prepared water-soluble UA-loaded dendrimer nanoparticles (UA-G4K NPs) non-cytotoxic on HeLa cells, with promising physicochemical properties for their clinical applications. In this work, with the aim of developing a new antibacterial agent based on UA, UA-G4K has been tested on different strains of the Enterococcus genus, including marine isolates, toward which UA-G4K has shown minimum inhibitory concentrations (MICs) very low (0.5–4.3 µM), regardless of their resistance to antibiotics. Time-kill experiments, in addition to confirming the previously reported bactericidal activity of UA against E. faecium, also established it for UA-G4K. Furthermore, cytotoxicity experiments on human keratinocytes revealed that nanomanipulation of UA significantly reduced the cytotoxicity of UA, providing UA-G4K NPs with very high LD50 (96.4 µM) and selectivity indices, which were in the range 22.4–192.8, depending on the enterococcal strain tested. Due to its physicochemical and biological properties, UA-G4K could be seriously evaluated as a novel oral-administrable therapeutic option for tackling difficult-to-treat enterococcal infections.
Optimising Artificial Moss Growth for Environmental Studies in the Mediterranean Area
Authors: Varela Zulema , Real Carlos , Branquinho Cristina , do Paço Teresa Afonso , Cruz de Carvalho Ricardo
Published: November 2021

Bryophytes are poikilohydric organisms that play a key role in ecosystems, while some of them are also resistant to drought and environmental disturbances but present a slow growth rate. Moss culture in the laboratory can be a very useful tool for ecological restoration or the development of urban green spaces (roof and wall) in the Mediterranean region. Therefore, we aim to: (i) determine the optimal culture conditions for the growth of four moss species present in the Mediterranean climate, such as Bryum argenteum, Hypnum cupressiforme, Tortella nitida, and Tortella squarrosa; (ii) study the optimal growth conditions of the invasive moss Campylopus introflexus to find out if it can be a threat to native species. Photoperiod does not seem to cause any recognisable pattern in moss growth. However, temperature produces more linear but slower growth at 15 °C than at 20 and 25 °C. In addition, the lower temperature produced faster maximum cover values within 5–8 weeks, with at least 60% of the culture area covered. The study concludes that the culture of moss artificially in the organic gardening substrate without fertilisers is feasible and could be of great help for further use in environmental projects to restore degraded ecosystems or to facilitate urban green spaces in the Mediterranean area. Moreover, this study concludes that C. introflexus could successfully occupy the niche of other native moss species, especially in degraded areas, in a future global change scenario.
Transformation of European Ash (Fraxinus excelsior L.) Callus as a Starting Point for Understanding the Molecular Basis of Ash Dieback
Authors: Hebda Anna , Liszka Aleksandra , Zgłobicki Piotr , Nawrot-Chorabik Katarzyna , Lyczakowski Jan J.
Published: November 2021

The population of European ash (Fraxinus excelsior L.) is currently facing the risk of collapse, mainly due to ash dieback, a disease caused by a pathogenic fungus, Hymenoscyphus fraxineus. To facilitate studies into the molecular basis of ash dieback and design breeding strategies for a generation of resistant trees, it is necessary to develop tools enabling the study of gene function in F. excelsior. Despite this, a method for the genetic engineering of F. excelsior is still missing. Here, we report the first successful genetic transformation of F. excelsior callus and a selection process enabling the formation of stable transgenic callus lines. The protocol relies on the use of Agrobacterium tumefaciens to transform callus tissue derived from embryos of F. excelsior. In our experiments, we used the β-glucuronidase (GUS) reporter system to demonstrate the transformation of callus cells and performed RT-PCR experiments to confirm the stable expression of the transgene. Since ash dieback threatens the long-term stability of many native F. excelsior populations, we hope that the transformation techniques described in this manuscript will facilitate rapid progress in uncovering the molecular basis of the disease and the validation of gene targets previously proposed to be linked to the resistance of trees to H. fraxineus pathogenicity.
Transmittance Control of a Water-Repellent-Coated Layer on a Tensioned Web in a Roll-to-Roll Slot-Die Coating System
Authors: Kim Seongyong , Jo Minho , Lee Jongsu , Lee Changwoo
Published: November 2021

Solar cells are important alternatives to fossil fuels for energy generation in today’s world, where the demand for alternative, renewable sources of energy is increasing. However, solar cells, which are installed outdoors, are susceptible to pollution by environmental factors. A solution to overcome this limitation involves coating solar cell surfaces with functional coatings. In this study, we propose a transmittance control method for a tensioned web in a roll-to-roll, transparent, water-repellent film coating. First, we analyzed the effects of process conditions on the transmittance and contact angle of the transparent water-repellent film during roll-to-roll slot-die coating. It was confirmed that the tension was the most dominant factor, followed by the coating gap. Through the tension control, the transmittance was changed by 3.27%, and the contact angle of the DI water was changed by 17.7°. In addition, it was confirmed that the transmittance was changed by 0.8% and the contact angle of DI water by 3.9° via the coating gap control. Based on these results, a transmittance prediction model was developed according to the tension and coating gap, and was then verified experimentally. Finally, a water-repellent film with a high transmittance of 89.77% was obtained using this model.
Study on Permeability Characteristics of Porous Transparent Gels Based on Synthetic Materials
Authors: Tao Gaoliang , Luo Qingshi , Huang Shaoping , Li Yi , Huang Zhe , Wu Zhijia , Zhang Fan , Dong Heming
Published: November 2021

Advanced knowledge of the permeability characteristics of transparent gels play a key role in providing a rational basis for the study of porous polymer permeability and the research on the migration behavior of superpolymer solutions. Thus, a new type of transparent gel was prepared to simulate porous media, with aim to observe and analyze the permeability characteristics of transparent gel under the conditions of our experimental design by combining a transparent soil test and simple particle image velocimetry. The experimental results showed that the permeability of the transparent gel was similar to that through actual soil. The permeability coefficients of the transparent gel under different pressure gradients varied greatly early in the experimental cycle, while changing only slightly afterward, showing an overall trend of decreasing first and then stabilizing. With the increase of the mass ratio, the permeability coefficient of the sample decreased, the distribution of the low-velocity zone of the intercepted section became wider and tended to move upward. Differences in spatial position also caused different patterns of velocity and direction. The findings presented in this paper contribute to providing a new direction for the study of porous polymer permeability and the porous migration of superpolymer solutions.
Efficacy Analysis of In Situ Synthesis of Nanogold via Copper/Iodonium/Amine/Gold System under a Visible Light
Authors: Lin Jui-Teng , Lalevee Jacques , Liu Hsia-Wei
Published: November 2021

This article presents, for the first time, the kinetics and the general features of a photopolymerization system (under visible light), copper-complex/Iodonium/triethylamine/gold-chloride (orA/B/N/G), with initial concentrations of A0, B0, N0 and G0, based on the proposed mechanism of Tar et al. Analytic formulas were developed to explore the new features, including: (i) both free radical photopolymerization (FRP) efficacy and the production of nanogold (NG), which are proportional to the relative concentration ratios of (A0 + B0 + N0)/G0 and may be optimized for maximum efficacy; (ii) the two competing procedures of NG production and the efficacy of FRP, which can be tailored for an optimal system with nanogold in the polymer matrix; (iii) the FRP efficacy, which is contributed by three components given by the excited state of copper complex (T), and the radicals (R and S) produced by iodonium and amine, respectively; (iv) NG production, which is contributed by the coupling of T and radical (S) with gold ion; and (v) NG production, which has a transient state proportional to the light intensity and the concentration ratio A0/G0) + (N0/(K’M0), but also a steady-state independent of the light intensity.
Processing, Performance Properties, and Storage Stability of Ground Tire Rubber Modified by Dicumyl Peroxide and Ethylene-Vinyl Acetate Copolymers
Authors: Wiśniewska Paulina , Zedler Łukasz , Formela Krzysztof
Published: November 2021

In this paper, ground tire rubber was modified with dicumyl peroxide and a variable content (in the range of 0–15 phr) of ethylene-vinyl acetate copolymers characterized by different vinyl acetate contents (in the range of 18–39 wt.%). Modification of ground tire rubber was performed via an auto-thermal extrusion process in which heat was generated during internal shearing of the material inside the extruder barrel. The processing, performance properties, and storage stability of modified reclaimed ground tire rubber were evaluated based on specific mechanical energy, infrared camera images, an oscillating disc rheometer, tensile tests, equilibrium swelling, gas chromatography combined with a flame ionization detector, and gas chromatography with mass spectrometry. It was found that the developed formulas of modified GTR allowed the preparation of materials characterized by tensile strengths in the range of 2.6–9.3 MPa and elongation at break in the range of 78–225%. Moreover, the prepared materials showed good storage stability for at least three months and satisfied processability with commercial rubbers (natural rubber, styrene-butadiene rubber).
Development of Long Wavelength Light-Absorptive Homopolymers Based on Pentaazaphenalene by Regioselective Oxidative Polymerization
Authors: Watanabe Hiroyuki , Tanaka Kazuo , Chujo Yoshiki
Published: November 2021

We report the synthesis and absorption properties of homopolymers consisting of 1,3,4,6,9b-pentaazaphenalene (5AP). Oxidative polymerization in the Scholl reaction was accomplished, and various lengths of homopolymers can be isolated. It should be noted that we scarcely observed the generation of structural isomers at the connecting points, which is often observed in this type of reaction. Therefore, we were able to evaluate electronic structures of the synthesized homopolymers. In addition, it was observed that absorption bands were obtained in the longer wavelength region than the monomer. The computer calculation suggests that the highest occupied molecular orbital (HOMO) energy levels could be lowered by electronic interaction through spatially-separated HOMOs of 5AP. Moreover, we can evaluate the extension of the conjugated system through the meta-substituted skeleton and distance dependency of the main-chain conjugation.
A Review on Reinforcement Methods for Polymeric Materials Processed Using Fused Filament Fabrication (FFF)
Authors: Pratama Juan , Cahyono Sukmaji I. , Suyitno Suyitno , Muflikhun Muhammad A. , Salim Urip A. , Mahardika Muslim , Arifvianto Budi
Published: November 2021

Over the last few years, fused filament fabrication (FFF) has become one of the most promising and widely used techniques for the rapid prototyping process. A number of studies have also shown the possibility of FFF being used for the fabrication of functional products, such as biomedical implants and automotive components. However, the poor mechanical properties possessed by FFF-processed products are considered one of the major shortcomings of this technique. Over the last decade, many researchers have attempted to improve the mechanical properties of FFF-processed products using several strategies—for instance, by applying the short fiber reinforcement (SFR), continuous fiber reinforcement (CFR), powder addition reinforcement (PAR), vibration-assisted FFF (VA-FFF) methods, as well as annealing. In this paper, the details of all these reinforcement techniques are reviewed. The abilities of each method in improving tensile, flexural, and compressive strength are discussed.
Nanostructured Plasma Polymerized Fluorocarbon Films for Drop Coating Deposition Raman Spectroscopy (DCDRS) of Liposomes
Authors: Kuižová Alžbeta , Kuzminova Anna , Kylián Ondřej , Kočišová Eva
Published: November 2021

Raman spectroscopy is one of the most used biodetection techniques. However, its usability is hampered in the case of low concentrated substances because of the weak intensity of the Raman signal. To overcome this limitation, the use of drop coating deposition Raman spectroscopy (DCDRS), in which the liquid samples are allowed to dry into well-defined patterns where the non-volatile solutes are highly concentrated, is appropriate. This significantly improves the Raman sensitivity when compared to the conventional Raman signal from solution/suspension. As DCDRS performance strongly depends on the wetting properties of substrates, we demonstrate here that the smooth hydrophobic plasma polymerized fluorocarbon films prepared by magnetron sputtering (contact angle 108°) are well-suited for the DCDRS detection of liposomes. Furthermore, it was proved that even better improvement of the Raman signal might be achieved if the plasma polymer surfaces are roughened. In this case, 100% higher intensities of Raman signal are observed in comparison with smooth fluorocarbon films. As it is shown, this effect, which has no influence on the profile of Raman spectra, is connected with the increased hydrophobicity of nanostructured fluorocarbon films. This results in the formation of dried liposomal deposits with smaller diameters and higher preconcentration of liposomes.
The Dynamic Impact Response of 3D-Printed Polymeric Sandwich Structures with Lattice Cores: Numerical and Experimental Investigation
Authors: Jhou Shu-Yu , Hsu Ching-Chi , Yeh Jui-Chia
Published: November 2021

This paper proposes a dynamic drop weight impact simulation to predict the impact response of 3D printed polymeric sandwich structures using an explicit finite element (FE) approach. The lattice cores of sandwich structures were based on two unit cells, a body-centred cubic (BCC) and an edge-centred cubic (ECC). The deformation and the peak acceleration, referred to as the g-max score, were calculated to quantify their shock absorption characteristic. For the FE results verification, a falling mass impact test was conducted. The FE results were in good agreement with experimental measurements. The results suggested that the strut diameter, strut length, number and orientation, and the apparent material stiffness of the lattice cores had a significant effect on their deformation behavior and shock absorption capability. In addition, the BCC lattice core with a thinner strut diameter and low structural height might lead to poor shock absorption capability caused by structure collapse and border effect, which could be improved by increasing its apparent material stiffness. This dynamic drop impact simulation process could be applied across numerous industries such as footwear, sporting goods, personal protective equipment, packaging, or biomechanical implants.
Viscosity Analysis of Battery Electrode Slurry
Authors: Cushing Alex , Zheng Tianyue , Higa Kenneth , Liu Gao
Published: November 2021

We report the effects of component ratios and mixing time on electrode slurry viscosity. Three component quantities were varied: active material (graphite), conductive material (carbon black), and polymer binder (carboxymethyl cellulose, CMC). The slurries demonstrated shear-thinning behavior, and suspension properties stabilized after a relatively short mixing duration. However, micrographs of the slurries suggested their internal structures did not stabilize after the same mixing time. Increasing the content of polymer binder CMC caused the greatest viscosity increase compared to that of carbon black and graphite.
Non-Specific Interactions of Rhizospheric Microbial Communities Support the Establishment of Mimosa acutistipula var. ferrea in an Amazon Rehabilitating Mineland
Authors: Costa Paulo Henrique de Oliveira , Nascimento Sidney Vasconcelos do , Herrera Hector , Gastauer Markus , Ramos Silvio Junio , Caldeira Cecílio Frois , Oliveira Guilherme , Valadares Rafael Borges da Silva
Published: November 2021

Mimosa acutistipula var. ferrea (Fabaceae) is endemic to ferruginous tropical rocky outcrops in the eastern Amazon, also known as canga. Canga are often associated with mining activities and are the target of protection and rehabilitation projects. M. acutistipula stands out in this biodiversity hotspot with high growth rates, even in rehabilitating minelands (RMs). However, little is known about the diversity of soil microorganisms interacting with M. acutistipula in canga and RMs. This study analyzed the rhizosphere-associated bacterial and fungal microbial communities associated with M. acutistipula growing in an RM and a native shrub canga. The fungal phylum Ascomycota was the dominant taxa identified in the rhizosphere of the canga (RA: 98.1) and RM (RA: 93.1). The bacterial phyla Proteobacteria (RA: 54.3) and Acidobacteria (RA: 56.2) were the dominant taxa identified in the rhizosphere in the canga and RM, respectively. Beneficial genera such as Bradyrhizobium, Rhodoplanes, and Paraconiothyrium were identified in the rhizosphere of M. acutistipula in both areas. However, the analyses showed that the fungal and bacterial diversity differed between the rhizosphere of the canga and RM, and that the microbial taxa adapted to the canga (i.e., Rasamsonia, Scytalidium, Roseiarcus, and Rhodomicrobium) were lacking in the RM. This influences the microbe-mediated soil processes, affecting long-term rehabilitation success. The results showed that M. acutistipula established non-specific interactions with soil microorganisms, including beneficial taxa such as nitrogen-fixing bacteria, mycorrhizal fungi, and other beneficial endophytes, well known for their importance in plant adaptation and survival. High levels of microbe association and a plant’s ability to recruit a wide range of soil microorganisms help to explain M. acutistipula’s success in rehabilitating minelands.
A Satellite-Based Method for National Winter Wheat Yield Estimating in China
Authors: Fu Yangyang , Huang Jianxi , Shen Yanjun , Liu Shaomin , Huang Yong , Dong Jie , Han Wei , Ye Tao , Zhao Wenzhi , Yuan Wenping
Published: November 2021

Satellite-based models have tremendous potential for monitoring crop production because satellite data can provide temporally and spatially continuous crop growth information at large scale. This study used a satellite-based vegetation production model (i.e., eddy covariance light use efficiency, EC-LUE) to estimate national winter wheat gross primary production, and then combined this model with the harvest index (ratio of aboveground biomass to yield) to convert the estimated winter wheat production to yield. Specifically, considering the spatial differences of the harvest index, we used a cross-validation method to invert the harvest index of winter wheat among counties, municipalities and provinces. Using the field-surveyed and statistical yield data, we evaluated the model performance, and found the model could explain more than 50% of the spatial variations of the yield both in field-surveyed regions and most administrative units. Overall, the mean absolute percentage errors of the yield are less than 20% in most counties, municipalities and provinces, and the mean absolute percentage errors for the production of winter wheat at the national scale is 4.06%. This study demonstrates that a satellite-based model is an alternative method for crop yield estimation on a larger scale.
Regional Ranking of Marine Turtle Nesting in Remote Western Australia by Integrating Traditional Ecological Knowledge and Remote Sensing
Authors: Tucker Anton , Pendoley Kellie , Murray Kathy , Loewenthal Graham , Barber Chris , Denda Jai , Lincoln Gina , Mathews Dean , Oades Daniel , Whiting Scott , Rangers Miriuwung , Rangers Balanggarra , Rangers Wunambal , Rangers Dambimangari , Rangers Mayala , Rangers Bardi , Rangers Nyul , Rangers Yawuru , Rangers Karajarri , Rangers Nyangumarta , Rangers Ngarla
Published: November 2021

Western Australia’s remote Kimberley coastline spans multiple Traditional Owner estates. Marine turtle nesting distribution and abundance in Indigenous Protected Areas and newly declared Marine Parks were assessed by aerial photogrammetry surveys for the Austral summer and winter nesting seasons. Images of nesting tracks were quantified in the lab and verified by ad hoc ground patrols. The rankings of log-scaled plots of track abundance and density give guidance to regional co-management planning. Spatial and temporal differences were detected in that remoter islands had higher nesting usage and few terrestrial predators. The surveys found year-round green turtle nesting peaking in summer, as well as spatial boundaries to the summer and winter flatback stocks. Summer surveys recorded 126.2 island activities per km and 17.7 mainland activities per km. Winter surveys recorded 65.3 island activities per km and quantified a known winter mainland rookery with 888 tracks/km. The three highest density rookeries were found to be winter flatback turtles at Cape Domett, summer green turtles at the Lacepede Islands and summer flatback turtles at Eighty Mile Beach. Moderate to lesser density nesting by summer green turtles and winter flatback turtles occurred in the North Kimberley offshore islands. Traditional Ecological Knowledge and ground-based surveys verified the harder-to-detect species (olive ridley or hawksbill turtles) with irregular nesting, low track persistence and non-aggregated nesting. Higher-density rookeries may provide locations for long-term monitoring using repeated aerial or ground surveys; however, the sparse or infrequently nesting species require insights gleaned by Tradition Ecological Knowledge. Common and conspicuous nesters are easily detected and ranked, but better-informed co-management requires additional ground surveys or surveys timed with the reproductive peaks of rarer species.
The Spatiotemporal Implications of Urbanization for Urban Heat Islands in Beijing: A Predictive Approach Based on CA–Markov Modeling (2004–2050)
Authors: Amir Siddique Muhammad , Wang Yu , Xu Ninghan , Ullah Nadeem , Zeng Peng
Published: November 2021

The rapid increase in infrastructural development in populated areas has had numerous adverse impacts. The rise in land surface temperature (LST) and its associated damage to urban ecological systems result from urban development. Understanding the current and future LST phenomenon and its relationship to landscape composition and land use/cover (LUC) changes is critical to developing policies to mitigate the disastrous impacts of urban heat islands (UHIs) on urban ecosystems. Using remote sensing and GIS data, this study assessed the multi-scale relationship of LUCC and LST of the cosmopolitan exponentially growing area of Beijing, China. We investigated the impacts of LUC on LST in urban agglomeration for a time series (2004–2019) of Landsat data using Classification and Regression Trees (CART) and a single channel algorithm (SCA), respectively. We built a CA–Markov model to forecast future (2025 and 2050) LUCC and LST spatial patterns. Our results indicate that the cumulative changes in an urban area (UA) increased by about 908.15 km2 (5%), and 11% of vegetation area (VA) decreased from 2004 to 2019. The correlation coefficient of LUCC including vegetation, water bodies, and built-up areas with LST had values of r = −0.155 (p > 0.419), −0.809 (p = 0.000), and 0.526 (p = 0.003), respectively. The results surrounding future forecasts revealed an estimated 2309.55 km2 (14%) decrease in vegetation (urban and forest), while an expansion of 1194.78 km2 (8%) was predicted for a built-up area from 2019 to 2050. This decrease in vegetation cover and expansion of settlements would likely cause a rise of about ~5.74 °C to ~9.66 °C in temperature. These findings strongly support the hypothesis that LST is directly related to the vegetation index. In conclusion, the estimated overall increase of 7.5 °C in LST was predicted from 2019–2050, which is alarming for the urban community’s environmental health. The present results provide insight into sustainable environmental development through effective urban planning of Beijing and other urban hotspots.
Unsupervised Deep Learning for Landslide Detection from Multispectral Sentinel-2 Imagery
Authors: Shahabi Hejar , Rahimzad Maryam , Tavakkoli Piralilou Sepideh , Ghorbanzadeh Omid , Homayouni Saied , Blaschke Thomas , Lim Samsung , Ghamisi Pedram
Published: November 2021

This paper proposes a new approach based on an unsupervised deep learning (DL) model for landslide detection. Recently, supervised DL models using convolutional neural networks (CNN) have been widely studied for landslide detection. Even though these models provide robust performance and reliable results, they depend highly on a large labeled dataset for their training step. As an alternative, in this paper, we developed an unsupervised learning model by employing a convolutional auto-encoder (CAE) to deal with the problem of limited labeled data for training. The CAE was used to learn and extract the abstract and high-level features without using training data. To assess the performance of the proposed approach, we used Sentinel-2 imagery and a digital elevation model (DEM) to map landslides in three different case studies in India, China, and Taiwan. Using minimum noise fraction (MNF) transformation, we reduced the multispectral dimension to three features containing more than 80% of scene information. Next, these features were stacked with slope data and NDVI as inputs to the CAE model. The Huber reconstruction loss was used to evaluate the inputs. We achieved reconstruction losses ranging from 0.10 to 0.147 for the MNF features, slope, and NDVI stack for all three study areas. The mini-batch K-means clustering method was used to cluster the features into two to five classes. To evaluate the impact of deep features on landslide detection, we first clustered a stack of MNF features, slope, and NDVI, then the same ones plus with the deep features. For all cases, clustering based on deep features provided the highest precision, recall, F1-score, and mean intersection over the union in landslide detection.
Uncertainty Introduced by Darkening Agents in the Lunar Regolith: An Unmixing Perspective
Authors: Hess Marcel , Wilhelm Thorsten , Wöhler Christian , Wohlfarth Kay
Published: November 2021

On the Moon, in the near infrared wavelength range, spectral diagnostic features such as the 1-μm and 2-μm absorption bands can be used to estimate abundances of the constituent minerals. However, there are several factors that can darken the overall spectrum and dampen the absorption bands. Namely, (1) space weathering, (2) grain size, (3) porosity, and (4) mineral darkening agents such as ilmenite have similar effects on the measured spectrum. This makes spectral unmixing on the Moon a particularly challenging task. Here, we try to model the influence of space weathering and mineral darkening agents and infer the uncertainties introduced by these factors using a Markov Chain Monte Carlo method. Laboratory and synthetic mixtures can successfully be characterized by this approach. We find that the abundance of ilmenite, plagioclase, clino-pyroxenes and olivine cannot be inferred accurately without additional knowledge for very mature spectra. The Bayesian approach to spectral unmixing enables us to include prior knowledge in the problem without imposing hard constraints. Other data sources, such as gamma-ray spectroscopy, can contribute valuable information about the elemental abundances. We here find that setting a prior on TiO2 and Al2O3 can mitigate many of the uncertainties, but large uncertainties still remain for dark mature lunar spectra. This illustrates that spectral unmixing on the Moon is an ill posed problem and that probabilistic methods are important tools that provide information about the uncertainties, that, in turn, help to interpret the results and their reliability.
Classifying Crop Types Using Two Generations of Hyperspectral Sensors (Hyperion and DESIS) with Machine Learning on the Cloud
Authors: Aneece Itiya , Thenkabail Prasad S.
Published: November 2021

Advances in spaceborne hyperspectral (HS) remote sensing, cloud-computing, and machine learning can help measure, model, map and monitor agricultural crops to address global food and water security issues, such as by providing accurate estimates of crop area and yield to model agricultural productivity. Leveraging these advances, we used the Earth Observing-1 (EO-1) Hyperion historical archive and the new generation DLR Earth Sensing Imaging Spectrometer (DESIS) data to evaluate the performance of hyperspectral narrowbands in classifying major agricultural crops of the U.S. with machine learning (ML) on Google Earth Engine (GEE). EO-1 Hyperion images from the 2010–2013 growing seasons and DESIS images from the 2019 growing season were used to classify three world crops (corn, soybean, and winter wheat) along with other crops and non-crops near Ponca City, Oklahoma, USA. The supervised classification algorithms: Random Forest (RF), Support Vector Machine (SVM), and Naive Bayes (NB), and the unsupervised clustering algorithm WekaXMeans (WXM) were run using selected optimal Hyperion and DESIS HS narrowbands (HNBs). RF and SVM returned the highest overall producer’s, and user’s accuracies, with the performances of NB and WXM being substantially lower. The best accuracies were achieved with two or three images throughout the growing season, especially a combination of an earlier month (June or July) and a later month (August or September). The narrow 2.55 nm bandwidth of DESIS provided numerous spectral features along the 400–1000 nm spectral range relative to smoother Hyperion spectral signatures with 10 nm bandwidth in the 400–2500 nm spectral range. Out of 235 DESIS HNBs, 29 were deemed optimal for agricultural study. Advances in ML and cloud-computing can greatly facilitate HS data analysis, especially as more HS datasets, tools, and algorithms become available on the Cloud.
Assessing Non-Photosynthetic Cropland Biomass from Spaceborne Hyperspectral Imagery
Authors: Berger Katja , Hank Tobias , Halabuk Andrej , Rivera-Caicedo Juan Pablo , Wocher Matthias , Mojses Matej , Gerhátová Katarina , Tagliabue Giulia , Dolz Miguel Morata , Venteo Ana Belen Pascual , Verrelst Jochem
Published: November 2021

Non-photosynthetic vegetation (NPV) biomass has been identified as a priority variable for upcoming spaceborne imaging spectroscopy missions, calling for a quantitative estimation of lignocellulosic plant material as opposed to the sole indication of surface coverage. Therefore, we propose a hybrid model for the retrieval of non-photosynthetic cropland biomass. The workflow included coupling the leaf optical model PROSPECT-PRO with the canopy reflectance model 4SAIL, which allowed us to simulate NPV biomass from carbon-based constituents (CBC) and leaf area index (LAI). PROSAIL-PRO provided a training database for a Gaussian process regression (GPR) algorithm, simulating a wide range of non-photosynthetic vegetation states. Active learning was employed to reduce and optimize the training data set. In addition, we applied spectral dimensionality reduction to condense essential information of non-photosynthetic signals. The resulting NPV-GPR model was successfully validated against soybean field data with normalized root mean square error (nRMSE) of 13.4% and a coefficient of determination (R2) of 0.85. To demonstrate mapping capability, the NPV-GPR model was tested on a PRISMA hyperspectral image acquired over agricultural areas in the North of Munich, Germany. Reliable estimates were mainly achieved over senescent vegetation areas as suggested by model uncertainties. The proposed workflow is the first step towards the quantification of non-photosynthetic cropland biomass as a next-generation product from near-term operational missions, such as CHIME.
Paris-CARLA-3D: A Real and Synthetic Outdoor Point Cloud Dataset for Challenging Tasks in 3D Mapping
Authors: Deschaud Jean-Emmanuel , Duque David , Richa Jean Pierre , Velasco-Forero Santiago , Marcotegui Beatriz , Goulette François
Published: November 2021

Paris-CARLA-3D is a dataset of several dense colored point clouds of outdoor environments built by a mobile LiDAR and camera system. The data are composed of two sets with synthetic data from the open source CARLA simulator (700 million points) and real data acquired in the city of Paris (60 million points), hence the name Paris-CARLA-3D. One of the advantages of this dataset is to have simulated the same LiDAR and camera platform in the open source CARLA simulator as the one used to produce the real data. In addition, manual annotation of the classes using the semantic tags of CARLA was performed on the real data, allowing the testing of transfer methods from the synthetic to the real data. The objective of this dataset is to provide a challenging dataset to evaluate and improve methods on difficult vision tasks for the 3D mapping of outdoor environments: semantic segmentation, instance segmentation, and scene completion. For each task, we describe the evaluation protocol as well as the experiments carried out to establish a baseline.
Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features
Authors: Malekzadeh Anis , Zare Assef , Yaghoobi Mahdi , Kobravi Hamid-Reza , Alizadehsani Roohallah
Published: November 2021

Epilepsy is a brain disorder disease that affects people’s quality of life. Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper provides a computer-aided diagnosis system (CADS) for the automatic diagnosis of epileptic seizures in EEG signals. The proposed method consists of three steps, including preprocessing, feature extraction, and classification. In order to perform the simulations, the Bonn and Freiburg datasets are used. Firstly, we used a band-pass filter with 0.5–40 Hz cut-off frequency for removal artifacts of the EEG datasets. Tunable-Q Wavelet Transform (TQWT) is used for EEG signal decomposition. In the second step, various linear and nonlinear features are extracted from TQWT sub-bands. In this step, various statistical, frequency, and nonlinear features are extracted from the sub-bands. The nonlinear features used are based on fractal dimensions (FDs) and entropy theories. In the classification step, different approaches based on conventional machine learning (ML) and deep learning (DL) are discussed. In this step, a CNN–RNN-based DL method with the number of layers proposed is applied. The extracted features have been fed to the input of the proposed CNN–RNN model, and satisfactory results have been reported. In the classification step, the K-fold cross-validation with k = 10 is employed to demonstrate the effectiveness of the proposed CNN–RNN classification procedure. The results revealed that the proposed CNN–RNN method for Bonn and Freiburg datasets achieved an accuracy of 99.71% and 99.13%, respectively.
Posture Assessment in Dentistry for Different Visual Aids Using 2D Markers
Authors: Pispero Alberto , Marcon Marco , Ghezzi Carlo , Massironi Domenico , Varoni Elena Maria , Tubaro Stefano , Lodi Giovanni
Published: November 2021

Attention and awareness towards musculoskeletal disorders (MSDs) in the dental profession has increased considerably in the last few years. From recent literature reviews, it appears that the prevalence of MSDs in dentists concerns between 64 and 93%. In our clinical trial, we have assessed the dentist posture during the extraction of 90 third lower molars depending on whether the operator performs the intervention by the use of the operating microscope, surgical loupes, or with the naked eye. In particular, we analyzed the evolution of the body posture during different interventions evaluating the impact of visual aids with respect to naked eye interventions. The presented posture assessment approach is based on 3D acquisitions of the upper body, based on planar markers, which allows us to discriminate spatial displacements up to 2 mm in translation and 1 degree in rotation. We found a significant reduction of neck bending in interventions using visual aids, in particular for those performed with the microscope. We further investigated the impact of different postures on MSD risk using a widely adopted evaluation tool for ergonomic investigations of workplaces, named (RULA) Rapid Upper Limb Assessment. The analysis performed in this clinical trial is based on a 3D marker tracker that is able to follow a surgeon’s upper limbs during interventions. The method highlighted pros and cons of different approaches.
Development and Testing of a UAV Laser Scanner and Multispectral Camera System for Eco-Geomorphic Applications
Authors: Tomsett Christopher , Leyland Julian
Published: November 2021

While Uncrewed Aerial Vehicle (UAV) systems and camera sensors are routinely deployed in conjunction with Structure from Motion (SfM) techniques to derive 3D models of fluvial systems, in the presence of vegetation these techniques are subject to large errors. This is because of the high structural complexity of vegetation and inability of processing techniques to identify bare earth points in vegetated areas. Furthermore, for eco-geomorphic applications where characterization of the vegetation is an important aim when collecting fluvial survey data, the issues are compounded, and an alternative survey method is required. Laser Scanning techniques have been shown to be a suitable technique for discretizing both bare earth and vegetation, owing to the high spatial density of collected data and the ability of some systems to deliver dual (e.g., first and last) returns. Herein we detail the development and testing of a UAV mounted LiDAR and Multispectral camera system and processing workflow, with application to a specific river field location and reference to eco-hydraulic research generally. We show that the system and data processing workflow has the ability to detect bare earth, vegetation structure and NDVI type outputs which are superior to SfM outputs alone, and which are shown to be more accurate and repeatable, with a level of detection of under 0.1 m. These characteristics of the developed sensor package and workflows offer great potential for future eco-geomorphic research.
Use of a Portable Inertial Measurement Unit as an Evaluation Method for Supraspinatus Muscle: Proposed Normative Values
Authors: Pérez-de la Cruz Sagrario
Published: November 2021

Treatment protocols do not specify an appropriate weight for rehabilitating the shoulder joint. The purpose of this study was to establish normative values for the shoulder abduction range of motion and recommended weights to be used in the rehabilitation process after injury to the supraspinatus muscle. Fifty-eight volunteers were assessed using the DyCare system. A test was conducted by lifting the arm to a 90° angle and having the participants lift different weights. The range of motion was similar for both sexes, suggesting that sex had no influence on this variable. Regarding the use of weights, men did not show as much stability in their movement execution, with a high dispersion seen in values between zero and three kilograms of weight, reaching a maximum weight of six kilograms. However, women showed good joint stability from the beginning of the test, with values that remained constant as weight increased up to a maximum of five kilograms. In conclusion, no major differences were observed in supraspinatus muscle injury recovery according to sex. However, differences were observed in the amount of weight that was necessary and appropriate to allow the participants to recover their muscular strength and avoid relapses.
Three-Dimensional Magnetic Induction Tomography: Practical Implementation for Imaging throughout the Depth of a Low Conductive and Voluminous Body
Authors: Klein Martin , Erni Daniel , Rueter Dirk
Published: November 2021

Magnetic induction tomography (MIT) is a contactless, low-energy method used to visualize the conductivity distribution inside a body under examination. A particularly demanding task is the three-dimensional (3D) imaging of voluminous bodies in the biomedical impedance regime. While successful MIT simulations have been reported for this regime, practical demonstration over the entire depth of weakly conductive bodies is technically difficult and has not yet been reported, particularly in terms of more realistic requirements. Poor sensitivity in the central regions critically affects the measurements. However, a recently simulated MIT scanner with a sinusoidal excitation field topology promises improved sensitivity (>20 dB) from the interior. On this basis, a large and fast 3D MIT scanner was practically realized in this study. Close agreement between theoretical forward calculations and experimental measurements underline the technical performance of the sensor system, and the previously only simulated progress is hereby confirmed. This allows 3D reconstructions from practical measurements to be presented over the entire depth of a voluminous body phantom with tissue-like conductivity and dimensions similar to a human torso. This feasibility demonstration takes MIT a step further toward the quick 3D mapping of a low conductive and voluminous object, for example, for rapid, harmless and contactless thorax or lung diagnostics.
Presentation Attack Detection on Limited-Resource Devices Using Deep Neural Classifiers Trained on Consistent Spectrogram Fragments
Authors: Kubicki Kacper , Kapusta Paweł , Ślot Krzysztof
Published: November 2021

The presented paper is concerned with detection of presentation attacks against unsupervised remote biometric speaker verification, using a well-known challenge–response scheme. We propose a novel approach to convolutional phoneme classifier training, which ensures high phoneme recognition accuracy even for significantly simplified network architectures, thus enabling efficient utterance verification on resource-limited hardware, such as mobile phones or embedded devices. We consider Deep Convolutional Neural Networks operating on windows of speech Mel-Spectrograms as a means for phoneme recognition, and we show that one can boost the performance of highly simplified neural architectures by modifying the principle underlying training set construction. Instead of generating training examples by slicing spectrograms using a sliding window, as it is commonly done, we propose to maximize the consistency of phoneme-related spectrogram structures that are to be learned, by choosing only spectrogram chunks from the central regions of phoneme articulation intervals. This approach enables better utilization of the limited capacity of the considered simplified networks, as it significantly reduces a within-class data scatter. We show that neural architectures comprising as few as dozens of thousands parameters can successfully—with accuracy of up to 76%, solve the 39-phoneme recognition task (we use the English language TIMIT database for experimental verification of the method). We also show that ensembling of simple classifiers, using a basic bagging method, boosts the recognition accuracy by another 2–3%, offering Phoneme Error Rates at the level of 23%, which approaches the accuracy of the state-of-the-art deep neural architectures that are one to two orders of magnitude more complex than the proposed solution. This, in turn, enables executing reliable presentation attack detection, based on just few-syllable long challenges on highly resource-limited computing hardware.
A Convolutional Autoencoder Topology for Classification in High-Dimensional Noisy Image Datasets
Authors: Pintelas Emmanuel , Livieris Ioannis E. , Pintelas Panagiotis E.
Published: November 2021

Deep convolutional neural networks have shown remarkable performance in the image classification domain. However, Deep Learning models are vulnerable to noise and redundant information encapsulated into the high-dimensional raw input images, leading to unstable and unreliable predictions. Autoencoders constitute an unsupervised dimensionality reduction technique, proven to filter out noise and redundant information and create robust and stable feature representations. In this work, in order to resolve the problem of DL models’ vulnerability, we propose a convolutional autoencoder topological model for compressing and filtering out noise and redundant information from initial high dimensionality input images and then feeding this compressed output into convolutional neural networks. Our results reveal the efficiency of the proposed approach, leading to a significant performance improvement compared to Deep Learning models trained with the initial raw images.
Flight Planning Optimization of Multiple UAVs for Internet of Things
Authors: Rodrigues Lucas , Riker André , Ribeiro Maria , Both Cristiano , Sousa Filipe , Moreira Waldir , Cardoso Kleber , Oliveira-Jr Antonio
Published: November 2021

This article presents an approach to autonomous flight planning of Unmanned Aerial Vehicles (UAVs)-Drones as data collectors to the Internet of Things (IoT). We have proposed a model for only one aircraft, as well as for multiple ones. A clustering technique that extends the scope of the number of IoT devices (e.g., sensors) visited by UAVs is also addressed. The flight plan generated from the model focuses on preventing breakdowns due to a lack of battery charge to maximize the number of nodes visited. In addition to the drone autonomous flight planning, a data storage limitation aspect is also considered. We have presented the energy consumption of drones based on the aerodynamic characteristics of the type of aircraft. Simulations show the algorithm’s behavior in generating routes, and the model is evaluated using a reliability metric.