bluuebunny/crossref_metadata_2025 huggingface dataset:
A Design of Experiment (DoE) Approach to Model the Yield and Chemical Composition of Ajowan (Trachyspermum ammi L.) Essential Oil Obtained by Microwave-Assisted Extraction
Authors: Mazzara Eugenia , Scortichini Serena , Fiorini Dennis , Maggi Filippo , Petrelli Riccardo , Cappellacci Loredana , Morgese Giuseppe , Morshedloo Mohammad Reza , Palmieri Giovanni Filippo , Cespi Marco
Published: August 2021

Ajowan (Trachyspermum ammi L.) is a spice traditionally used in Middle Eastern medicine and contains a valuable essential oil (EO) exploited in different fields, such as pharmaceutics, agrochemicals and food additives. This EO is mostly characterized by the thymol to which most of its biological properties are related. Given the economic value of ajowan and its increasing demand across the globe, the extraction method used for its EO is of paramount importance in terms of quality and quantity of the final product. In the present study, we used the design of experiment (DoE) approach to study and optimize the extraction of the ajowan EO using the microwave-assisted extraction (MAE), a novel extraction technique with high efficiency, low energy consumption, short process length and low environmental impact. A two-step DoE (screening followed by surface response methodology) was used to reduce the number of experiments and to improve the cost/benefit ratio. Reliable mathematical models, relating the more relevant EO features with the extraction conditions, were obtained and used to identify the best experimental conditions able to maximize the yield and thymol concentration. The optimized MAE procedure assures an EO with a higher yield and thymol amount compared with the standard hydrodistillation procedure.
Micellar Antibiotics of Bacillus
Authors: Ferreira William T. , Hong Huynh A. , Hess Mateusz , Adams James R. G. , Wood Hannah , Bakun Karolina , Tan Sisareuth , Baccigalupi Loredana , Ferrari Enrico , Brisson Alain , Ricca Ezio , Teresa Rejas María , Meijer Wilfried J. J. , Soloviev Mikhail , Cutting Simon M.
Published: August 2021

Members of the Bacillus genus, particularly the “Bacillus subtilis group”, are known to produce amphipathic lipopeptides with biosurfactant activity. This includes the surfactins, fengycins and iturins that have been associated with antibacterial, antifungal, and anti-viral properties. We have screened a large collection of Bacillus, isolated from human, animal, estuarine water and soil samples and found that the most potent lipopeptide producers are members of the species Bacillus velezensis. B. velezensis lipopeptides exhibited anti-bacterial activity which was localised on the surface of both vegetative cells and spores. Interestingly, lipopeptide micelles (6–10 nm diameter) were detectable in strains exhibiting the highest levels of activity. Micelles were stable (heat and gastric stable) and shown to entrap other antimicrobials produced by the host bacterium (exampled here was the dipeptide antibiotic chlorotetaine). Commercially acquired lipopeptides did not exhibit similar levels of inhibitory activity and we suspect that micelle formation may relate to the particular isomeric forms produced by individual bacteria. Using naturally produced micelle formulations we demonstrated that they could entrap antimicrobial compounds (e.g., clindamycin, vancomycin and resveratrol). Micellar incorporation of antibiotics increased activity. Bacillus is a prolific producer of antimicrobials, and this phenomenon could be exploited naturally to augment antimicrobial activity. From an applied perspective, the ability to readily produce Bacillus micelles and formulate with drugs enables a possible strategy for enhanced drug delivery.
Unravelling the Photoprotection Capacity of Resveratrol on Histidine Oxidation
Authors: Neyra Recky Jael R. , Dántola M. Laura , Lorente Carolina
Published: August 2021

Exposure to sun radiation causes great oxidative stress and activates a numerous of defense mechanisms in living systems, such as the synthesis of antioxidants. Resveratrol (RSV), a naturally occurring stilbene molecule, has antioxidant properties and is synthesized in large amounts when plants are under high oxidative stress. Likewise, under UV and visible radiation, biomolecules are oxidized, losing their physiological properties and, therefore, avoiding the harmful effects of solar radiation is crucial in order to preserve the functionality of cellular components. In proteins, one essential component that is often susceptible to degradation is the amino acid histidine (His), which can be modified via several oxidizing mechanisms. In this article, we evaluate the photoprotection capacity of RSV in photosensitized oxidation of His, which is initiated with a one-electron transfer reaction, yielding the His radical cation (His•+). The photoprotective properties of RSV are evaluated using kinetics analysis during steady-state irradiation and laser flash photolysis experiments. The experimental results reveal that the presence of RSV in the solution causes an evident decrease of the His consumption initial rates as a result of a reaction between His•+ and RSV that recovers the amino acid. In addition, we conclude that during its antioxidant action, RSV is consumed being a sacrificial antioxidant.
Effects of Temperature and Wetness Duration on Infection by Coniella diplodiella, the Fungus Causing White Rot of Grape Berries
Authors: Ji Tao , Languasco Luca , Li Ming , Rossi Vittorio
Published: August 2021

Grapevine white rot, caused by Coniella diplodiella, can severely damage berries during ripening. The effects of temperature and wetness duration on the infection severity of C. diplodiella were investigated by artificially inoculating grape berries through via infection pathways (uninjured and injured berries, and through pedicels). The effect of temperature on incubation was also studied, as was that of inoculum dose. Injured berries were affected sooner than uninjured berries, even though 100% of the berries inoculated with C. diplodiella conidia became rotted whether injured or not; infection through pedicels was less severe. On injured berries, the disease increased as the inoculum dose increased. Irrespective of the infection pathway, 1 h of wetness was sufficient to cause infection at any temperature tested (10–35 °C); with the optimal temperature being 23.8 °C. The length of incubation was shorter for injured berries than for uninjured ones, and was shorter at 25–35 °C than at lower temperatures; the shortest incubation period was 14 h for injured berries at 30 °C. Mathematical equations were developed that fit the data, with R2 = 0.93 for infection through any infection pathway, and R2 = 0.98 for incubation on injured berries, which could be used to predict infection period and, therefore, to schedule fungicide applications.
Morphological Diversity, Genetic Characterization, and Phytochemical Assessment of the Cypriot Tomato Germplasm
Authors: Athinodorou Filio , Foukas Petros , Tsaniklidis Georgios , Kotsiras Anastasios , Chrysargyris Antonios , Delis Costas , Kyratzis Angelos C. , Tzortzakis Nikolaos , Nikoloudakis Nikolaos
Published: August 2021

Tomato (Solanum lycopersicum L.) is considered one of the most valuable and versatile vegetable crops globally and also serves as a significant model species for fruit developmental biology. Despite its significance, a severe genetic bottleneck and intense selection of genotypes with specific qualitative traits have resulted in the prevalence of a restricted number of (geno)types, also causing a lack of diversity across widespread cultivated types. As a result, the re-emergence of landraces as well as traditional and heirloom varieties is largely acknowledged as a countermeasure to restore phenotypic, phytochemical and genetic diversity while enriching the aroma/taste tomato palette. On those grounds, the Cypriot tomato germplasm was assessed and characterized. Ten landrace accessions were evaluated under greenhouse conditions and data were collected for 24 IPGRI discrete phenotypic traits. Grouping of accessions largely reflected the fruit shape and size; four different fruit types were recorded across accessions (flattened, heart-shaped, rounded and highly rounded). Moreover, a single run panel consisting of ten SSRs was developed and applied in order to genetically characterize 190 Cypriot genotypes and foreign heirloom varieties. Based on genetic indexes it was established that tomato landraces have a rather low level of heterogeneity and genetic variation. Finally, mineral and phytochemical analyses were conducted in order to estimate biochemical attributes (total phenolics, ascorbic acid, lycopene, β-carotene, total soluble content, titratable acidity) across genotypes; thus, ascertaining that the Cypriot panel has a high nutritional value. Due to the thermo-drought adaptation and tolerance of these genotypes, the current study serves as a roadmap for future breeding efforts in order to incorporate desirable traits or develop novel tomato lines combining resilience and alimentary value.
A Core35S Promoter of Cauliflower Mosaic Virus Drives More Efficient Replication of Turnip Crinkle Virus
Authors: Ali Md Emran , Waliullah Sumyya
Published: August 2021

The 35S promoter with a duplicated enhancer (frequently referred to as 2X35S) is a strong dicotyledonous plant-specific promoter commonly used in generating transgenic plants to enable high-level expression of genes of interest. It is also used to drive the initiation of RNA virus replication from viral cDNA, with the consensus understanding that high levels of viral RNA production powered by 2X35S permit a more efficient initiation of virus replication. Here, we showed that the exact opposite is true. We found that, compared to the Core35S promoter, the 2X35S promoter-driven initiation of turnip crinkle virus (TCV) infection was delayed by at least 24 h. We first compared three versions of 35S promoter, namely 2X35S, 1X35S, and Core35S, for their ability to power the expression of a non-replicating green fluorescent protein (GFP) gene, and confirmed that 2X35S and Core35S correlated with the highest and lowest GFP expression, respectively. However, when inserted upstream of TCV cDNA, 2X35S-driven replication was not detected until 72 h post-inoculation (72 hpi) in inoculated leaves. By contrast, Core35S-driven replication was detected earlier at 48 hpi. A similar delay was also observed in systemically infected leaves (six versus four days post-inoculation). Combining our results, we hypothesized that the stronger 2X35S promoter might enable a higher accumulation of a TCV protein that became a repressor of TCV replication at higher cellular concentration. Extending from these results, we propose that the Core35S (or mini35S) promoter is likely a better choice for generating infectious cDNA clones of TCV.
Influence of Curly Leaf Trait on Cottonseed Micro-Nutrient Status in Cotton (Gossypium hirsutum L.) Lines
Authors: Bellaloui Nacer , Turley Rickie B. , Stetina Salliana R.
Published: August 2021

Cottonseed is a source of nutrients, including protein, oil, and macro- and micro-nutrients. Micro-nutrients such as boron (B), copper (Cu), iron (Fe), manganese (Mn), and zinc (Zn) are essential for plant and human health. Deficiencies of these micro-nutrients in soil lead to poor crop production and poor seed quality. Micro-nutrient deficiencies in the human diet lead to malnutrition and serious health issues. Therefore, identifying new cotton lines containing high nutritional qualities such as micro-nutrients, and understanding plant traits influencing micro-nutrients are essential. The objective of this research was to investigate the effects of leaf shape (curly leaf: CRL) on cottonseed B, Cu, Fe, Mn, Ni (nickel), and Zn in two near-isogenic cotton lines differing in leaf shape (DP 5690 wild-type with normal leaves and DP 5690 CRL). We also used Uzbek CRL, the source of the curly leaf trait, for comparison. A field experiment was conducted in 2014 and 2015 in Stoneville, MS, USA. The experiment was a randomized complete block design with three replicates. The results showed that, in 2014, both DP 5690 wild-type and Uzbek CRL had higher seed B, Cu, Fe, and Ni than in DP 5690 CRL. The accumulation of Mn and Zn in seeds of DP 5690 CRL was higher than in DP 5690 wild-type and Uzbek CRL. However, in 2015, the concentrations of B, Cu, Fe, and Ni, including Mn and Zn, were higher in both DP 5690 wild-type and Uzbek CRL than in DP 5690 CRL. Positive and negative correlations existed in 2014; however, only positive correlations existed between all nutrients in 2015. This research demonstrated that leaf shape can alter cottonseed micro-nutrients status. As Uzbek CRL behaved similar to wild-type, both leaf shape and other factors contributed to the alteration in seed micronutrients, affecting seed nutritional qualities. Therefore, leaf-shape partially contributed to the changes in micro-nutrients in cottonseed. The negative and positive correlations in 2014, and only positive correlations in 2015, were likely due to the heat difference between 2014 and 2015 as 2015 was warmer than 2014. Significant levels of seed micro-nutrients were shown between these lines, providing opportunities for breeders to select for high seed micro-nutrients in cotton. Additionally, the current research provides researchers with physiological information on the impact of leaf shape on seed nutritional quality. The leaf shape trait can also be used as a tool to study leaf development, physiological, biochemical, and morphological processes.
Novel Sources of Pre-Harvest Sprouting Resistance for Japonica Rice Improvement
Authors: Lee Jae-Sung , Chebotarov Dmytro , McNally Kenneth L. , Pede Valerien , Setiyono Tri Deri , Raquid Rency , Hyun Woong-Jo , Jeung Ji-Ung , Kohli Ajay , Mo Youngjun
Published: August 2021

Pre-harvest sprouting (PHS), induced by unexpected weather events, such as typhoons, at the late seed maturity stage, is becoming a serious threat to rice production, especially in the state of California, USA, Japan, and the Republic of Korea, where japonica varieties (mostly susceptible to PHS) are mainly cultivated. A projected economic loss by severe PHS in these three countries could range between 8–10 billion USD per year during the next 10 years. Here, we present promising rice germplasm with strong resistance to PHS that were selected from a diverse rice panel of accessions held in the International Rice Genebank (IRG) at the International Rice Research Institute (IRRI). To induce PHS, three panicle samples per accession were harvested at 20 and 30 days after flowering (DAF), respectively, and incubated at 100% relative humidity (RH), 30 °C in a growth chamber for 15 days. A genome-wide association (GWA) analysis using a 4.8 million single nucleotide polymorphisms (SNP) marker set was performed to identify loci and candidate genes conferring PHS resistance. Interestingly, two tropical japonica and four temperate japonica accessions showed outstanding PHS resistance as compared to tolerant indica accessions. Two major loci on chromosomes 1 and 4 were associated with PHS resistance. A priori candidate genes interactions with rice gene networks, which are based on the gene ontology (GO), co-expression, and other evidence, suggested that a key resistance mechanism is related to abscisic acid (ABA), gibberellic acid (GA), and auxin mediated signaling pathways.
Investigation of Chemical Profiles of Different Parts of Morus alba Using a Combination of Molecular Networking Methods with Mass Spectral Data from Two Ionization Modes of LC/MS
Authors: Choi Seong Yeon , Park Jinyoung , Kim Juyeol , Lee Jiho , Yang Heejung
Published: August 2021

Plants produce numerous secondary metabolites with diverse physicochemical properties. Because different parts of a single plant produce various components, several spectroscopic methods are necessary to inspect their chemical profiles. Mass spectral data are recognized as one of the most useful tools for analyzing components with a wide range of polarities. However, interpreting mass spectral data generated from positive and negative ionization modes is a challenging task because of the diverse chemical profiles of secondary metabolites. Herein, we combine and analyze mass spectral data generated in two ionization modes to detect as many metabolites as possible using the molecular networking approach. We selected different parts of a single plant, Morus alba (Moraceae), which are used in the functional food and medicinal herb industries. The mass spectral data generated from two ionization modes were combined and analyzed using various molecular networking workflows. We confirmed that our approach could be applied to simultaneously analyze the different types of secondary metabolites with different physicochemical properties.
Modulating the Thermoresponse of Polymer-Protein Conjugates with Hydrogels for Controlled Release
Authors: Huynh Vincent , Ifraimov Natalie , Wylie Ryan G.
Published: August 2021

Sustained release is being explored to increase plasma and tissue residence times of polymer-protein therapeutics for improved efficacy. Recently, poly(oligo(ethylene glycol) methyl ether methacrylate) (PEGMA) polymers have been established as potential PEG alternatives to further decrease immunogenicity and introduce responsive or sieving properties. We developed a drug delivery system that locally depresses the lower critical solution temperature (LCST) of PEGMA-protein conjugates within zwitterionic hydrogels for controlled release. Inside the hydrogel the conjugates partially aggregate through PEGMA-PEGMA chain interactions to limit their release rates, whereas conjugates outside of the hydrogel are completely solubilized. Release can therefore be tuned by altering hydrogel components and the PEGMA’s temperature sensitivity without the need for traditional controlled release mechanisms such as particle encapsulation or affinity interactions. Combining local LCST depression technology and degradable zwitterionic hydrogels, complete release of the conjugate was achieved over 13 days.
Properties of High-Density Fiberboard Bonded with Urea–Formaldehyde Resin and Ammonium Lignosulfonate as a Bio-Based Additive
Authors: Antov Petar , Savov Viktor , Trichkov Neno , Krišťák Ľuboš , Réh Roman , Papadopoulos Antonios N. , Taghiyari Hamid R. , Pizzi Antonio , Kunecová Daniela , Pachikova Marina
Published: August 2021

The potential of ammonium lignosulfonate (ALS) as an eco-friendly additive to urea–formaldehyde (UF) resin for manufacturing high-density fiberboard (HDF) panels with acceptable properties and low free formaldehyde emission was investigated in this work. The HDF panels were manufactured in the laboratory with very low UF resin content (4%) and ALS addition levels varying from 4% to 8% based on the mass of the dry wood fibers. The press factor applied was 15 s·mm−1. The physical properties (water absorption and thickness swelling), mechanical properties (bending strength, modulus of elasticity, and internal bond strength), and free formaldehyde emission were evaluated in accordance with the European standards. In general, the developed HDF panels exhibited acceptable physical and mechanical properties, fulfilling the standard requirements for HDF panels for use in load-bearing applications. Markedly, the laboratory-produced panels had low free formaldehyde emission ranging from 2.0 to 1.4 mg/100 g, thus fulfilling the requirements of the E0 and super E0 emission grades and confirming the positive effect of ALS as a formaldehyde scavenger. The thermal analyses performed, i.e., differential scanning calorimetry (DSC), thermal gravimetric analysis (TGA), and derivative thermogravimetry (DTG), also confirmed the main findings of the research. It was concluded that ALS as a bio-based, formaldehyde-free adhesive can be efficiently utilized as an eco-friendly additive to UF adhesive formulations for manufacturing wood-based panels under industrial conditions.
Bamboo Charcoal/Poly(L-lactide) Fiber Webs Prepared Using Laser-Heated Melt Electrospinning
Authors: Hou Zongzi , Itagaki Nahoko , Kobayashi Haruki , Tanaka Katsufumi , Takarada Wataru , Kikutani Takeshi , Takasaki Midori
Published: August 2021

Although several studies have reported that the addition of bamboo charcoal (BC) to polylactide (PLA) enhances the properties of PLA, to date, no study has been reported on the fabrication of ultrafine BC/poly(L-lactide) (PLLA) webs via electrospinning. Therefore, ultrafine fiber webs of PLLA and BC/PLLA were prepared using PLLA and BC/PLLA raw fibers via a novel laser electrospinning method. Ultrafine PLLA and BC/PLLA fibers with average diameters of approximately 1 μm and coefficients of variation of 13–23 and 20–46% were obtained. Via wide-angle X-ray diffraction (WAXD) analysis, highly oriented crystals were detected in the raw fibers; however, WAXD patterns of both PLLA and BC/PLLA webs implied an amorphous structure of PLLA. Polarizing microscopy images revealed that the webs comprised ultrafine fibers with uniform diameters and wide variations in birefringence. Temperature-modulated differential scanning calorimetry measurements indicated that the degree of order of the crystals in the fibers was lower and the molecules in the fibers had higher mobilities than those in the raw fibers. Transmittance of BC/PLLA webs with an area density of 2.6 mg/cm2 suggested that the addition of BC improved UV-shielding efficiencies.
Synthesis and Characterization of Conducting PANDB/χ-Al2O3 Core-Shell Nanocomposites by In Situ Polymerization
Authors: Chen Cheng-Ho , Lin Ying-Chen , Yen Fu-Su
Published: August 2021

Polyaniline doped with dodecylbenzenesulfonic acid/χ-aluminum oxide (PANDB/χ-Al2O3) conducting core-shell nanocomposites was synthesized via an in situ polymerization method in this study. PANDB was synthesized in the presence of dodecylbenzenesulfonic acid (DBSA), which functioned as a dopant and surfactant. The electrical conductivity of the conducting PANDB/χ-Al2O3 core-shell nanocomposite was approximately 1.7 × 10−1 S/cm when the aniline/χ-Al2O3 (AN/χ-Al2O3) weight ratio was 1.5. The transmission electron microscopy (TEM) results indicated that the χ-Al2O3 nanoflakes were thoroughly coated by PANDB to form the core-shell (χ-Al2O3-PANDB) structure. The TEM and field-emission scanning electron microscopy (FE-SEM) images of the conducting PANDB/χ-Al2O3 core-shell nanocomposites also indicated that the thickness of the PANDB layer (shell) could be increased as the weight ratio of AN/χ-Al2O3 was increased. In this study, the optimum weight ratio of AN/χ-Al2O3 was identified as 1.5. The conducting PANDB/χ-Al2O3 core-shell nanocomposite was then blended with water-based polyurethane (WPU) to form a conducting WPU/PANDB/χ-Al2O3 blend film. The resulting blend film has promising antistatic and electrostatic discharge (ESD) properties.
Improving Recycled Poly(lactic Acid) Biopolymer Properties by Chain Extension Using Block Copolymers Synthesized by Nitroxide-Mediated Polymerization (NMP)
Authors: Benvenuta-Tapia Juan José , Champagne Pascale , Tenorio-López José Alfredo , Vivaldo-Lima Eduardo , Guerrero-Santos Ramiro
Published: August 2021

The aim of this contribution is to assess the use poly(styrene-co-glycidyl methacrylate-b-styrene) copolymers synthesized by nitroxide mediated polymerization (NMP) as chain extenders in the recycling of poly(lactic acid) biopolyester. Concisely, the addition of such block copolymers during the melt processing of recycled poly(lactic acid) (rPLA) leads to important increases in the viscosity average molecular weight of modified polymeric materials. Molar masses increase from 31,000 g/mol for rPLA to 48,000 g mol−1 for the resulting rPLA/copolymer blends (bPLA). Fortuitously, this last value is nearly the same as the one for pristine PLA, which constitutes a first piece of evidence of the molar mass increase of the recycled biopolymer. Thermograms of chain extended rPLA show significant decreases in cold crystallization temperature and higher crystallinity degrees due to the chain extension process using NMP-synthesized copolymers. It was found that increasing epoxide content in the NMP-synthesized copolymers leads to increased degrees of crystallinity and lower cold crystallization temperatures. The rheological appraisal has shown that the addition of NMP synthesized copolymers markedly increases complex viscosity and elastic modulus of rPLA. Our results indicate that P(S-co-GMA)-b-S) copolymers act as efficient chain extenders of rPLA, likely due to the reaction between the epoxy groups present in P(S-co-GMA)-b-PS and the carboxyl acid groups present in rPLA. This reaction positively affects viscometric molar mass of PLA and its performance.
Overview on the Antimicrobial Activity and Biocompatibility of Sputtered Carbon-Based Coatings
Authors: Carvalho Isabel , Rodrigues Lisa , Lima Maria José , Carvalho Sandra , Cruz Sandra M. A.
Published: August 2021

Due to their outstanding properties, carbon-based structures have received much attention from the scientific community. Their applications are diverse and include use in coatings on self-lubricating systems for anti-wear situations, thin films deposited on prosthetic elements, catalysis structures, or water remediation devices. From these applications, the ones that require the most careful testing and improvement are biomedical applications. The biocompatibility and antibacterial issues of medical devices remain a concern, as several prostheses still fail after several years of implantation and biofilm formation remains a real risk to the success of a device. Sputtered deposition prevents the introduction of hazardous chemical elements during the preparation of coatings, and this technique is environmentally friendly. In addition, the mechanical properties of C-based coatings are remarkable. In this paper, the latest advances in sputtering methods and biocompatibility and antibacterial action for diamond-based carbon (DLC)-based coatings are reviewed and the greater outlook is then discussed.
An Insight into the Separation of 1,2-Propanediol, Ethylene Glycol, Acetol and Glycerol from an Aqueous Solution by Adsorption on Activated Carbon
Authors: Fortea Jorge , García Lucía , Ruiz Joaquín , Oliva Miriam , Arauzo Jesús
Published: August 2021

Glycerol conversion processes such as aqueous phase reforming and hydrogenolysis generate value-added compounds highly diluted in water. Because distillation is a high energy demand separation step, adsorption could be an attractive alternative to recover these chemicals. Adsorption isotherms of 1,2-propanediol, acetol, ethylene glycol and glycerol onto activated carbon were determined by batch adsorption experiments. These isotherms were fitted slightly better to the Freundlich equation than to the Langmuir equation. Acetol is the compound with the highest adsorption at concentrations smaller than 1 M. Properties of the adsorbate such as the −OH group number, chain length, molecular size and dipole moment, besides characteristics of the adsorbent such as the surface area, oxygen and ash content, are considered to explain the observed results. Moreover, adsorption experiments were performed with mixtures of compounds and it was determined that the molar amount adsorbed is less than predicted from the adsorption isotherms of the individual compounds treated separately. In addition, the influence of the activated carbon thermal pre-treatment temperature on the adsorption capacity has been studied, the optimum being 800 °C. An analysis of the influence of the activated carbon characteristics showed that the most important parameters are the total pore volume and the ash content.
Using Canopy Measurements to Predict Soybean Seed Yield
Authors: Schmitz Peder K. , Kandel Hans J.
Published: August 2021

Predicting soybean [Glycine max (L.) Merr.] seed yield is of interest for crop producers to make important agronomic and economic decisions. Evaluating the soybean canopy across a range of common agronomic practices, using canopy measurements, provides a large inference for soybean producers. The individual and synergistic relationships between fractional green canopy cover (FGCC), photosynthetically active radiation (PAR) interception, and a normalized difference vegetative index (NDVI) measurements taken throughout the growing season to predict soybean seed yield in North Dakota, USA, were investigated in 12 environments. Canopy measurements were evaluated across early and late planting dates, 407,000 and 457,000 seeds ha−1 seeding rates, 0.5 and 0.8 relative maturities, and 30.5 and 61 cm row spacings. The single best yield predictor was an NDVI measurement at R5 (beginning of seed development) with a coefficient of determination of 0.65 followed by an FGCC measurement at R5 (R2 = 0.52). Stepwise and Lasso multiple regression methods were used to select the best prediction models using the canopy measurements explaining 69% and 67% of the variation in yield, respectively. Including plant density, which can be easily measured by a producer, with an individual canopy measurement did not improve the explanation in yield. Using FGCC to estimate yield across the growing season explained a range of 49% to 56% of yield variation, and a single FGCC measurement at R5 (R2 = 0.52) being the most efficient and practical method for a soybean producer to estimate yield.
UAV-Based Land Cover Classification for Hoverfly (Diptera: Syrphidae) Habitat Condition Assessment: A Case Study on Mt. Stara Planina (Serbia)
Authors: Ivošević Bojana , Lugonja Predrag , Brdar Sanja , Radulović Mirjana , Vujić Ante , Valente João
Published: August 2021

Habitat degradation, mostly caused by human impact, is one of the key drivers of biodiversity loss. This is a global problem, causing a decline in the number of pollinators, such as hoverflies. In the process of digitalizing ecological studies in Serbia, remote-sensing-based land cover classification has become a key component for both current and future research. Object-based land cover classification, using machine learning algorithms of very high resolution (VHR) imagery acquired by an unmanned aerial vehicle (UAV) was carried out in three different study sites on Mt. Stara Planina, Eastern Serbia. UAV land cover classified maps with seven land cover classes (trees, shrubs, meadows, road, water, agricultural land, and forest patches) were studied. Moreover, three different classification algorithms—support vector machine (SVM), random forest (RF), and k-NN (k-nearest neighbors)—were compared. This study shows that the random forest classifier performs better with respect to the other classifiers in all three study sites, with overall accuracy values ranging from 0.87 to 0.96. The overall results are robust to changes in labeling ground truth subsets. The obtained UAV land cover classified maps were compared with the Map of the Natural Vegetation of Europe (EPNV) and used to quantify habitat degradation and assess hoverfly species richness. It was concluded that the percentage of habitat degradation is primarily caused by anthropogenic pressure, thus affecting the richness of hoverfly species in the study sites. In order to enable research reproducibility, the datasets used in this study are made available in a public repository.
A Deep Learning Multimodal Method for Precipitation Estimation
Authors: Moraux Arthur , Dewitte Steven , Cornelis Bruno , Munteanu Adrian
Published: August 2021

To improve precipitation estimation accuracy, new methods, which are able to merge different precipitation measurement modalities, are necessary. In this study, we propose a deep learning method to merge rain gauge measurements with a ground-based radar composite and thermal infrared satellite imagery. The proposed convolutional neural network, composed of an encoder–decoder architecture, performs a multiscale analysis of the three input modalities to estimate simultaneously the rainfall probability and the precipitation rate value with a spatial resolution of 2 km. The training of our model and its performance evaluation are carried out on a dataset spanning 5 years from 2015 to 2019 and covering Belgium, the Netherlands, Germany and the North Sea. Our results for instantaneous precipitation detection, instantaneous precipitation rate estimation, and for daily rainfall accumulation estimation show that the best accuracy is obtained for the model combining all three modalities. The ablation study, done to compare every possible combination of the three modalities, shows that the combination of rain gauges measurements with radar data allows for a considerable increase in the accuracy of the precipitation estimation, and the addition of satellite imagery provides precipitation estimates where rain gauge and radar coverage are lacking. We also show that our multi-modal model significantly improves performance compared to the European radar composite product provided by OPERA and the quasi gauge-adjusted radar product RADOLAN provided by the DWD for precipitation rate estimation.
Unsupervised Learning in RSS-Based DFLT Using an EM Algorithm
Authors: Kaltiokallio Ossi , Hostettler Roland , Yiğitler Hüseyin , Valkama Mikko
Published: August 2021

Received signal strength (RSS) changes of static wireless nodes can be used for device-free localization and tracking (DFLT). Most RSS-based DFLT systems require access to calibration data, either RSS measurements from a time period when the area was not occupied by people, or measurements while a person stands in known locations. Such calibration periods can be very expensive in terms of time and effort, making system deployment and maintenance challenging. This paper develops an Expectation-Maximization (EM) algorithm based on Gaussian smoothing for estimating the unknown RSS model parameters, liberating the system from supervised training and calibration periods. To fully use the EM algorithm’s potential, a novel localization-and-tracking system is presented to estimate a target’s arbitrary trajectory. To demonstrate the effectiveness of the proposed approach, it is shown that: (i) the system requires no calibration period; (ii) the EM algorithm improves the accuracy of existing DFLT methods; (iii) it is computationally very efficient; and (iv) the system outperforms a state-of-the-art adaptive DFLT system in terms of tracking accuracy.
Computer-Aided Diagnosis Algorithm for Classification of Malignant Melanoma Using Deep Neural Networks
Authors: Kim Chan-Il , Hwang Seok-Min , Park Eun-Bin , Won Chang-Hee , Lee Jong-Ha
Published: August 2021

Malignant melanoma accounts for about 1–3% of all malignancies in the West, especially in the United States. More than 9000 people die each year. In general, it is difficult to characterize a skin lesion from a photograph. In this paper, we propose a deep learning-based computer-aided diagnostic algorithm for the classification of malignant melanoma and benign skin tumors from RGB channel skin images. The proposed deep learning model constitutes a tumor lesion segmentation model and a classification model of malignant melanoma. First, U-Net was used to classify skin lesions in dermoscopy images. We implement an algorithm to classify malignant melanoma and benign tumors using skin lesion images and expert labeling results from convolutional neural networks. The U-Net model achieved a dice similarity coefficient of 81.1% compared to the expert labeling results. The classification accuracy of malignant melanoma reached 80.06%. As a result, the proposed AI algorithm is expected to be utilized as a computer-aided diagnostic algorithm to help early detection of malignant melanoma.
Evaluation of Low-Cost GNSS Receiver under Demanding Conditions in RTK Network Mode
Authors: Janos Daniel , Kuras Przemysław
Published: August 2021

Positioning with low-cost GNSS (Global Navigation Satellite System) receivers is becoming increasingly popular in many engineering applications. In particular, dual-frequency receivers, which receive signals of all available satellite systems, offer great possibilities. The main objective of this research was to evaluate the accuracy of a position determination using low-cost receivers in different terrain conditions. The u-blox ZED-F9P receiver was used for testing, with the satellite signal supplied by both a dedicated u-blox ANN-MB-00 low-cost patch antenna and the Leica AS10 high-precision geodetic one. A professional Leica GS18T geodetic receiver was used to acquire reference satellite data. In addition, on the prepared test base, observations were made using the Leica MS50 precise total station, which provided higher accuracy and stability of measurement than satellite positioning. As a result, it was concluded that the ZED-F9P receiver equipped with a patch antenna is only suitable for precision measurements in conditions with high availability of open sky. However, the configuration of this receiver with a geodetic-grade antenna significantly improves the quality of results, beating even professional geodetic equipment. In most cases of the partially obscured horizon, a high precision positioning was obtained, making the ZED-F9P a valuable alternative to the high-end geodetic receivers in many applications.
Development and Progress in Sensors and Technologies for Human Emotion Recognition
Authors: Pal Shantanu , Mukhopadhyay Subhas , Suryadevara Nagender
Published: August 2021

With the advancement of human-computer interaction, robotics, and especially humanoid robots, there is an increasing trend for human-to-human communications over online platforms (e.g., zoom). This has become more significant in recent years due to the Covid-19 pandemic situation. The increased use of online platforms for communication signifies the need to build efficient and more interactive human emotion recognition systems. In a human emotion recognition system, the physiological signals of human beings are collected, analyzed, and processed with the help of dedicated learning techniques and algorithms. With the proliferation of emerging technologies, e.g., the Internet of Things (IoT), future Internet, and artificial intelligence, there is a high demand for building scalable, robust, efficient, and trustworthy human recognition systems. In this paper, we present the development and progress in sensors and technologies to detect human emotions. We review the state-of-the-art sensors used for human emotion recognition and different types of activity monitoring. We present the design challenges and provide practical references of such human emotion recognition systems in the real world. Finally, we discuss the current trends in applications and explore the future research directions to address issues, e.g., scalability, security, trust, privacy, transparency, and decentralization.
The Application of a Sonic Probe Extensometer for the Detection of Rock Salt Flow Field in Underground Convergence Monitoring
Authors: Szczerbowski Zbigniew , Niedbalski Zbigniew
Published: August 2021

Special regulations have been laid down to establish the principles and requirements for the safety and serviceability of old mining workings which are adapted for tourism. To comply with these regulations the measurements were taken in the Bochnia Salt Mine, which has been in use for 800 years. The presented work demonstrates the use of a sonic probe extensometer in connection with the obtained results of displacement measurements in intact rocks surrounding the gallery. There were also test measurements carried out for determination of the real accuracy of the instrument. The presented study of deformations detected by electromagnetic extensometer measurements is presumed to be the first time that research has been made in salt mines operating in rock mass affected by tectonic stress. The paper presents the process of rock salt flow into the gallery observed over a period of 3 years. It is an unprecedented depiction of salt deformation subjected to natural stresses. One of the more surprising results presented here is the discovery of the occurrence of a specific distribution of strain around the measured gallery. The results of measurements showed that the southern part of the intact rock mass surrounding the passage is more compressed (strain rate 3.6 mm/m/year) than the northern one (strain rate 1.6 mm/m/year). This illustrates the presence and influence of additional tectonic effects resulting from the Carpathian push. These observations represent a new kind of research into tectonic stress and tectonic activity in underground measurements.
Spectral-Coding-Based Compressive Single-Pixel NIR Spectroscopy in the Sub-Millisecond Regime
Authors: Gattinger Paul , Zorin Ivan , Rankl Christian , Brandstetter Markus
Published: August 2021

In this contribution, we present a high-speed, multiplex, grating spectrometer based on a spectral coding approach that is founded on principles of compressive sensing. The spectrometer employs a single-pixel InGaAs detector to measure the signals encoded by an amplitude spatial light modulator (digital micromirror device, DMD). This approach leads to a speed advantage and multiplex sensitivity advantage atypical for standard dispersive systems. Exploiting the 18.2 kHz pattern rate of the DMD, we demonstrated 4.2 ms acquisition times for full spectra with a bandwidth of 450 nm (5250–4300 cm−1; 1.9–2.33 µm). Due to the programmability of the DMD, spectral regions of interest can be chosen freely, thus reducing acquisition times further, down to the sub-millisecond regime. The adjustable resolving power of the system accessed by means of computer simulations is discussed, quantified for different measurement modes, and verified by comparison with a state-of-the-art Fourier-transform infrared spectrometer. We show measurements of characteristic polymer absorption bands in different operation regimes of the spectrometer. The theoretical multiplex advantage of 8 was experimentally verified by comparison of the noise behavior of the spectral coding approach and a standard line-scan approach.
Deep Learning for Classifying Physical Activities from Accelerometer Data
Authors: Nunavath Vimala , Johansen Sahand , Johannessen Tommy Sandtorv , Jiao Lei , Hansen Bjørge Herman , Berntsen Sveinung , Goodwin Morten
Published: August 2021

Physical inactivity increases the risk of many adverse health conditions, including the world’s major non-communicable diseases, such as coronary heart disease, type 2 diabetes, and breast and colon cancers, shortening life expectancy. There are minimal medical care and personal trainers’ methods to monitor a patient’s actual physical activity types. To improve activity monitoring, we propose an artificial-intelligence-based approach to classify physical movement activity patterns. In more detail, we employ two deep learning (DL) methods, namely a deep feed-forward neural network (DNN) and a deep recurrent neural network (RNN) for this purpose. We evaluate the two models on two physical movement datasets collected from several volunteers who carried tri-axial accelerometer sensors. The first dataset is from the UCI machine learning repository, which contains 14 different activities-of-daily-life (ADL) and is collected from 16 volunteers who carried a single wrist-worn tri-axial accelerometer. The second dataset includes ten other ADLs and is gathered from eight volunteers who placed the sensors on their hips. Our experiment results show that the RNN model provides accurate performance compared to the state-of-the-art methods in classifying the fundamental movement patterns with an overall accuracy of 84.89% and an overall F1-score of 82.56%. The results indicate that our method provides the medical doctors and trainers a promising way to track and understand a patient’s physical activities precisely for better treatment.
Using an Internet of Behaviours to Study How Air Pollution Can Affect People’s Activities of Daily Living: A Case Study of Beijing, China
Authors: Zhang Guangyuan , Poslad Stefan , Rui Xiaoping , Yu Guangxia , Fan Yonglei , Song Xianfeng , Li Runkui
Published: August 2021

This study aims to quantitatively model rather than to presuppose whether or not air pollution in Beijing (China) affects people’s activities of daily living (ADLs) based on an Internet of Behaviours (IoB), in which IoT sensor data can signal environmental events that can change human behaviour on mass. Peoples’ density distribution computed by call detail records (CDRs) and air quality data are used to build a fixed effect model (FEM) to analyse the influence of air pollution on four types of ADLs. The following four effects are discovered: Air pollution negatively impacts people going sightseeing in the afternoon; has a positive impact on people staying-in, in the morning and the middle of the day. Air pollution lowers people’s desire to go to restaurants for lunch, but far less so in the evening. As air quality worsens, people tend to decrease their walking and cycling and tend to travel more by bus or subway. We also find a monotonically decreasing nonlinear relationship between air quality index and the average CDR-based distance for each person of two citizen groups that go walking or cycling. Our key and novel contributions are that we first define IoB as a ubiquitous concept. Based on this, we propose a methodology to better understand the link between bad air pollution events and citizens’ activities of daily life. We applied this methodology in the first comprehensive study that provides quantitative evidence of the actual effect, not the presumed effect, that air pollution can significantly affect a wide range of citizens’ activities of daily living.
Scene Recognition Using Deep Softpool Capsule Network Based on Residual Diverse Branch Block
Authors: Wang Chunyuan , Wu Yang , Wang Yihan , Chen Yiping
Published: August 2021

With the improvement of the quality and resolution of remote sensing (RS) images, scene recognition tasks have played an important role in the RS community. However, due to the special bird’s eye view image acquisition mode of imaging sensors, it is still challenging to construct a discriminate representation of diverse and complex scenes to improve RS image recognition performance. Capsule networks that can learn the spatial relationship between the features in an image has a good image classification performance. However, the original capsule network is not suitable for images with a complex background. To address the above issues, this paper proposes a novel end-to-end capsule network termed DS-CapsNet, in which a new multi-scale feature enhancement module and a new Caps-SoftPool method are advanced by aggregating the advantageous attributes of the residual convolution architecture, Diverse Branch Block (DBB), Squeeze and Excitation (SE) block, and the Caps-SoftPool method. By using the residual DBB, multiscale features can be extracted and fused to recover a semantic strong feature representation. By adopting SE, the informative features are emphasized, and the less salient features are weakened. The new Caps-SoftPool method can reduce the number of parameters that are needed in order to prevent an over-fitting problem. The novel DS-CapsNet achieves a competitive and promising performance for RS image recognition by using high-quality and robust capsule representation. The extensive experiments on two challenging datasets, AID and NWPU-RESISC45, demonstrate the robustness and superiority of the proposed DS-CapsNet in scene recognition tasks.