bluuebunny/crossref_metadata_2025 huggingface dataset:
Fruit Morphology, Citrulline, and Arginine Levels in Diverse Watermelon (Citrullus lanatus) Germplasm Collections
Authors: Assefa Awraris Derbie , Hur On-Sook , Ro Na-Young , Lee Jae-Eun , Hwang Ae-Jin , Kim Bich-Saem , Rhee Ju-Hee , Yi Jung-Yoon , Kim Ji-Hyun , Lee Ho-Sun , Sung Jung-Sook , Kim Myung-Kon , Noh Jae-Jong
Published: August 2020

Watermelon (Citrullus lanatus) is a non-seasonal, economically important, cucurbit cultivated throughout the world, with Asia as a continent contributing the most. As part of the effort to diversify watermelon genetic resources in the already cultivated group, this study was devoted to providing baseline data on morphological quality traits and health-beneficial phytonutrients of watermelon germplasm collections, thereby promoting watermelon research and cultivation programs. To this end, we reported morphological traits, citrulline, and arginine levels of watermelon genetic resources obtained from the gene bank of Agrobiodiversity Center, Republic of Korea, and discussed the relationships between each. Diverse characteristics were observed among many of the traits, but most of the genetic resources (>90%) were either red or pink-fleshed. Korean originated fruits contained intermediate levels of soluble solid content (SSC) while the USA, Russian, Tajikistan, Turkmenistan, Taiwan, and Uruguay originated fruits had generally the highest levels of soluble solids. The citrulline and arginine contents determined using the High Performance Liquid Chromatography (HPLC) method ranged from 6.9 to 52.1 mg/g (average, 27.3 mg/g) and 1.8 to 21.3 mg/g (average, 9.8 mg/g), respectively. The citrulline content determined using the Citrulline Assay Kit ranged from 6.5 to 42.8 mg/g (average, 27.0 mg/g). Resources with high citrulline and arginine levels contained low SSC, whereas red- and pink-colored flesh samples had less citrulline compared to yellow and orange.
Interactive Role of Silicon and Plant–Rhizobacteria Mitigating Abiotic Stresses: A New Approach for Sustainable Agriculture and Climate Change
Authors: Verma Krishan K. , Song Xiu-Peng , Li Dong-Mei , Singh Munna , Rajput Vishnu D. , Malviya Mukesh Kumar , Minkina Tatiana , Singh Rajesh Kumar , Singh Pratiksha , Li Yang-Rui
Published: August 2020

Abiotic stresses are the major constraints in agricultural crop production across the globe. The use of some plant–microbe interactions are established as an environment friendly way of enhancing crop productivity, and improving plant development and tolerance to abiotic stresses by direct or indirect mechanisms. Silicon (Si) can also stimulate plant growth and mitigate environmental stresses, and it is not detrimental to plants and is devoid of environmental contamination even if applied in excess quantity. In the present review, we elaborate the interactive application of Si and plant growth promoting rhizobacteria (PGPRs) as an ecologically sound practice to increase the plant growth rate in unfavorable situations, in the presence of abiotic stresses. Experiments investigating the combined use of Si and PGPRs on plants to cope with abiotic stresses can be helpful in the future for agricultural sustainability.
Monitoring the Efficiency of Rhazya stricta L. Plants in Phytoremediation of Heavy Metal-Contaminated Soil
Authors: Azab Ehab , Hegazy Ahmad K.
Published: August 2020

Heavy metal-contaminated soil constitutes many environmental concerns. The toxic nature of heavy metals poses serious threats to human health and the ecosystem. Decontamination of the polluted soil by phytoremediation is of fundamental importance. Vegetation is an appealing and cost-effective green technology for the large-scale phytoremediation of polluted soils. In this paper, a greenhouse experiment was carried out to test the potential of Rhazya stricta as a heavy metal phytoremediator in polluted soil. Plants were grown for three months in pots filled with soils treated with the heavy metals Cd, Pb, Cu, and Zn at rates of 10, 50, and 100 mg/kg. The bioaccumulation factor (BCF) and translocation factor (TF) were calculated to detect the ability of R. stricta to accumulate and transfer heavy metals from soil to plant organs. The results showed that under increasing levels of soil pollution, the bioconcentration of Cd and Zn heavy metals showed the highest values in plant roots followed by leaves, whereas in the case of Pb and Cu, roots showed the highest values followed by stems. Heavy metals accumulation was higher in roots than in stems and leaves. The BCF of Zn reached the highest values in roots and stems for 10 mg/kg soil treatment, followed by the BCFs of Cd, Cu, and Pb. The TF for the different heavy metal pollutants’ concentrations was less than unity, suggesting that the plants remediate pollutants by phytostabilization. The TF values ranged from higher to lower were in the order Zn > Cu > Cd > Pb. The rapid growth of R. stricta and its tolerance of heavy metals, as well as its ability to absorb and accumulate metals within the plant, recommends its use in the phytoremediation of slightly polluted soils in arid lands by limiting the heavy metals transport.
Multivariate Analysis of Amino Acids and Health Beneficial Properties of Cantaloupe Varieties Grown in Six Locations in the United States
Authors: Singh Jashbir , Metrani Rita , Jayaprakasha Guddadarangavvanahally K. , Crosby Kevin M. , Ravishankar Sadhana , Patil Bhimanagouda S.
Published: August 2020

Cantaloupe is a good dietary source of amino acids, including γ-aminobutyric acid (GABA), glutamine, and citrulline. However, the levels of these amino acids vary among different cantaloupe varieties grown in different locations. Understanding the variation in amino acid contents provides fundamentally important information for quality control and improving melon varieties. To examine this variation, we measured the amino acid contents in cantaloupes grown in six locations in the United States (Texas, Georgia, North Carolina, California, Indiana, and Arizona). Principal component analyses were applied to analyze the effect of growing location on the amino acid profiles in different varieties. The GABA content ranged from 1006.14 ± 64.77 to 3187.12 ± 64.96 µg/g and citrulline ranged from 92.65 ± 9.52 to 464.75 ± 34.97 µg/g depending on the variety and location. Total phenolic contents, α-amylase inhibition, and antioxidant activities were also measured. Tuscan type Da Vinci had significantly higher phenolic contents in Arizona (381.99 ± 16.21 µg/g) but had the lowest level when grown in California (224.56 ± 14.62 µg/g). Our analyses showed significant differences in amino acid levels, phenolics contents, and antioxidant activity in the cantaloupe varieties based on the growing location. These findings underline the importance of considering growing location in the selection and improvement of cantaloupe varieties.
Secondary Bioactive Metabolites from Plant-Derived Food Byproducts through Ecopharmacognostic Approaches: A Bound Phenolic Case Study
Authors: Burlini Ilaria , Sacchetti Gianni
Published: August 2020

The climate emergency and the risks to biodiversity that the planet is facing nowadays, have made the management of food resources increasingly complex but potentially interesting. According to FAO, one-third of the edible parts of food produced throughout the whole food supply chain gets lost or wasted globally every year. At the same time, demographic growth makes it necessary to change course toward sustainable economic development in order to satisfy market demands. The European Union supported the idea of a Circular Economy from 2015 and arranged annual Action Plans toward a greener, climate-neutral economy. Following the biorefinery concept, food waste becomes byproducts that can be recovered and exploited as high added-value materials for industrial applications. The use of sustainable extraction processes to manage food byproducts is a task that research has to support through the development of low environmental impact strategies. This review, therefore, aims to take stock of the possibilities of extracting molecules from food waste biomass following ecopharmacognostic approaches inspired by green chemistry guidelines. In particular, the use of innovative hybrid techniques to maximize yields and minimize the environmental impact of processes is reviewed, with a focus on bound phenolic extractions.
Assessing the Effect of Diesel Fuel on the Seed Viability and Germination of Medicago sativa Using the Event-Time Model
Authors: Eze Michael O. , Hose Grant C. , George Simon C.
Published: August 2020

The remediation of contaminated sites using plant-based techniques has gained increasing attention in recent decades. However, information on the effects of contaminant imbibition on seed viability and germination rates are often lacking in the literature. To this end, our research investigated, by means of an event-time model, the effect of diesel fuel imbibition on the seed viability and germination rate of Medicago sativa, a plant species with great potential for remediation of organic contaminants. The event-time model provided an accurate and biologically relevant method for analysing germination data. Our results reveal that the direct imbibition of diesel fuel by M. sativa seeds for ≤48 h, or their exposure to soil diesel fuel concentrations of 0–10 g/kg diesel fuel, affects their germination rates, as shown by increasing t50 values from 90.6 (±2.78) to 114.2 (±2.67) hours, without significantly affecting seed viability. On the other hand, diesel fuel imbibition of longer duration, or the exposure of M. sativa seeds to ≥20 g/kg diesel fuel-contaminated soils, leads to no further effect on time to seed emergence. Instead, these conditions compromise seed viability, resulting in a decrease in the proportion of germinated seeds from 0.91 (±0.03) in 10 g/kg diesel fuel contaminated soil to 0.84 (±0.04) and 0.70 (±0.05) in 20 and 30 g/kg diesel fuel-contaminated soils, respectively. The fact that low concentrations of diesel fuel and 0–48 h of direct imbibition delayed seed emergence without adversely affecting the percentage of viable seeds suggests that this inhibitory effect on germination at low diesel fuel exposure could be attributed more to physical constraints rather than biological damage on the seeds. The models used in this study provide an accurate and biologically relevant method for the analyses of germination data. This is vital since expensive germination experiments, be it in the field of toxicology or agriculture, deserve to be accurately analysed.
Artemisia absinthium L.—Importance in the History of Medicine, the Latest Advances in Phytochemistry and Therapeutical, Cosmetological and Culinary Uses
Authors: Szopa Agnieszka , Pajor Joanna , Klin Paweł , Rzepiela Agnieszka , Elansary Hosam O. , Al-Mana Fahed A. , Mattar Mohamed A. , Ekiert Halina
Published: August 2020

Artemisia absinthium—wormwood (Asteraceae)—is a very important species in the history of medicine, formerly described in medieval Europe as “the most important master against all exhaustions”. It is a species known as a medicinal plant in Europe and also in West Asia and North America. The raw material obtained from this species is Absinthii herba and Artemisiae absinthii aetheroleum. The main substances responsible for the biological activity of the herb are: the essential oil, bitter sesquiterpenoid lactones, flavonoids, other bitterness-imparting compounds, azulenes, phenolic acids, tannins and lignans. In the official European medicine, the species is used in both allopathy and homeopathy. In the traditional Asian and European medicine, it has been used as an effective agent in gastrointestinal ailments and also in the treatment of helminthiasis, anaemia, insomnia, bladder diseases, difficult-to-heal wounds, and fever. Today, numerous other directions of biological activity of the components of this species have been demonstrated and confirmed by scientific research, such as antiprotozoal, antibacterial, antifungal, anti-ulcer, hepatoprotective, anti-inflammatory, immunomodulatory, cytotoxic, analgesic, neuroprotective, anti-depressant, procognitive, neurotrophic, and cell membrane stabilizing and antioxidant activities. A. absinthium is also making a successful career as a cosmetic plant. In addition, the importance of this species as a spice plant and valuable additive in the alcohol industry (famous absinthe and vermouth-type wines) has not decreased. The species has also become an object of biotechnological research.
Nutritional Content and Antioxidant Capacity of the Seed and the Epicarp in Different Ecotypes of Pistacia atlantica Desf. Subsp. atlantica
Authors: Labdelli Amina , Rebiai Abdelkrim , Tahirine Mohammed , Adda Ahmed , Merah Othmane
Published: August 2020

Phenolic compounds are secondary metabolites that occur naturally in all plants. Seeds are among the richest organs of plants in phytochemicals, vitamins and minerals. These compounds and their biological activities are of great importance for human health. This study aimed to analyze the phenolic composition and their antioxidant activity in the seeds and epicarps of six Algerian populations of Pistacia atlantica Desf. subsp. atlantica growing along an aridity gradient from semi-arid to Saharan environmental conditions. Higher phenolic contents were observed in epicarp compared to seeds whatever the ecotype. The highest phenolic content of seeds and epicarps was observed in ecotype of Djelfa and the lowest values in Tiaret (T-Z). Phenolic composition, measured by reversed-phase high-performance liquid chromatography (HPLC), showed that quercetin in epicarp, gallic and chlorogenic acids in seeds were the most present in all ecotypes. Large differences were observed between ecotypes for nutritional values. Seeds were rich in flavonoids, proteins, carbohydrates and essential elements such as potassium, calcium, phosphorus and iron. These results highlighted the potential importance of Atlas pistachio fruits as a source of essential compounds that contribute to human health. Moreover, this underused species may serve a potential source for antioxidant components for alimentation and cosmetics purposes.
Two-Layer Functional Coatings of Chitosan Particles with Embedded Catechin and Pomegranate Extracts for Potential Active Packaging
Authors: Potrč Sanja , Kraševac Glaser Tjaša , Vesel Alenka , Poklar Ulrih Nataša , Fras Zemljič Lidija
Published: August 2020

Two-layer functional coatings for polyethylene (PE) and polypropylene (PP) films were developed for the active packaging concept. Prior to coating, the polymer films were activated by O2 and NH3 plasma to increase their surface free energy and to improve the binding capacity and stability of the coatings. The first layer was prepared from a macromolecular chitosan solution, while the second (upper) layer contained chitosan particles with embedded catechin or pomegranate extract. Functionalized films were analyzed physico-chemically to elemental composition using ATR-FTIR spectroscopy and XPS. Further, oxygen permeability and wettability (Contact Angle) were examined. The antimicrobial properties were analyzed by the standard ISO 22196 method, while the antioxidative properties were determined with an ABTS assay. Functionalized films show excellent antioxidative and antimicrobial efficacy. A huge decrease in oxygen permeability was achieved in addition. Moreover, a desorption experiment was also performed, confirming that the migration profile of a compound from the surfaces was in accordance with the required overall migration limit. All these properties indicate the great potential of the developed active films/foils for end-uses in food packaging.
Viscoelastic Behavior of Glass-Fiber-Reinforced Silicone Composites Exposed to Cyclic Loading
Authors: Beter Julia , Schrittesser Bernd , Lechner Bernhard , Mansouri Mohammad Reza , Marano Claudia , Fuchs Peter Filipp , Pinter Gerald
Published: August 2020

The aim of this work was to analyze the influence of fibers on the mechanical behavior of fiber-reinforced elastomers under cyclic loading. Thus, the focus was on the characterization of structure–property interactions, in particular the dynamic mechanical and viscoelastic behavior. Endless twill-woven glass fibers were chosen as the reinforcement, along with silicone as the matrix material. For the characterization of the flexible composites, a novel testing device was developed. Apart from the conventional dynamic mechanical analysis, in which the effect of the fiber orientation was also considered, modified step cycle tests were conducted under tensile loading. The material viscoelastic behavior was studied, evaluating both the stress relaxation response and the capability of the material to dissipate energy under straining. The effects of the displacement rate of the strain level, the amplitude of the strain applied in the loading–unloading step cycle test, and the number of the applied cycles were evaluated. The results revealed that an optimized fiber orientation leads to 30-fold enhanced stiffness, along with 10 times higher bearable stress. The findings demonstrated that tailored reinforced elastomers with endless fibers have a strong influence on the mechanical performance, affecting the structural properties significantly.
Microfluidic Technology for the Production of Well-Ordered Porous Polymer Scaffolds
Authors: Zhao Pei , Wang Jianchun , Li Yan , Wang Xueying , Chen Chengmin , Liu Guangxia
Published: August 2020

Advances in tissue engineering (TE) have revealed that porosity architectures, such as pore shape, pore size and pore interconnectivity are the key morphological properties of scaffolds. Well-ordered porous polymer scaffolds, which have uniform pore size, regular geometric shape, high porosity and good pore interconnectivity, facilitate the loading and distribution of active biomolecules, as well as cell adhesion, proliferation and migration. However, these are difficult to prepare by traditional methods and the existing well-ordered porous scaffold preparation methods require expensive experimental equipment or cumbersome preparation steps. Generally, droplet-based microfluidics, which generates and manipulates discrete droplets through immiscible multiphase flows inside microchannels, has emerged as a versatile tool for generation of well-ordered porous materials. This short review details this novel method and the latest developments in well-ordered porous scaffold preparation via microfluidic technology. The pore structure and properties of microfluidic scaffolds are discussed in depth, laying the foundation for further research and application in TE. Furthermore, we outline the bottlenecks and future developments in this particular field, and a brief outlook on the future development of microfluidic technique for scaffold fabrication is presented.
Grafting Poly(Methyl Methacrylate) (PMMA) from Cork via Atom Transfer Radical Polymerization (ATRP) towards Higher Quality of Three-Dimensional (3D) Printed PMMA/Cork-g-PMMA Materials
Authors: Lacerda Paula S. S. , Gama Nuno , Freire Carmen S. R. , Silvestre Armando J. D. , Barros-Timmons Ana
Published: August 2020

Cork is a unique material and its by-products are attracting an ever-growing interest for preparing new materials in an attempt to extend the outstanding properties of cork toward innovative and high value applications. Yet, the miscibility of cork particles with thermoplastic matrices is not easy due to its low density and surface properties. Here, cork is functionalized with poly(methyl methacrylate) (PMMA) via atom transfer radical polymerization (ATRP) to yield cork grafted with PMMA chains particles (cork-g-PMMA). Both the ATRP macroinitiator and the cork-g-PMMA obtained are fully characterized by Fourier-transform infrared spectroscopy (FT-IR), 13C cross-polarized magic-angle spinning solid-state nuclear magnetic resonance (13C CP/MAS solid state NMR), scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDX), X-ray diffraction (XRD) and thermogravimetric analyses (TGA). The functionalized cork particles are then blended with commercial PMMA to afford cork-g-PMMA/PMMA. To compare, cork also is mixed with PMMA and the ensuing cork/PMMA sample and its morphology, thermal, and mechanical properties are compared with those of cork-g-PMMA/PMMA and commercial PMMA. The cork surface modification via ATRP of the methyl methacrylate (MMA) yields better dispersion in the matrix. Consequently, a blend with enhanced mechanical performance, higher thermal stability, and a higher melt flow index (MFI) is obtained when compared to the blend prepared using unmodified particles. The similarity of the MFI of cork-g-PMMA/PMMA to that of PMMA suggests good printability. Indeed, a three-dimensional (3D) printed specimen is obtained confirming that grafting using ATRP is a promising route for the preparation of high quality 3D printed products.
Heteronuclear Dirhodium-Gold Anionic Complexes: Polymeric Chains and Discrete Units
Authors: Fernandez-Bartolome Estefania , Cruz Paula , Galán Laura Abad , Cortijo Miguel , Delgado-Martínez Patricia , González-Prieto Rodrigo , Priego José L. , Jiménez-Aparicio Reyes
Published: August 2020

In this article, we report on the synthesis and characterization of the tetracarboxylatodirhodium(II) complexes [Rh2(μ–O2CCH2OMe)4(THF)2] (1) and [Rh2(μ–O2CC6H4–p–CMe3)4(OH2)2] (2) by metathesis reaction of [Rh2(μ–O2CMe)4] with the corresponding ligand acting also as the reaction solvent. The reaction of the corresponding tetracarboxylato precursor, [Rh2(μ–O2CR)4], with PPh4[Au(CN)2] at room temperature, yielded the one-dimensional polymers (PPh4)n[Rh2(μ–O2CR)4Au(CN)2]n (R = Me (3), CH2OMe (4), CH2OEt (5)) and the non-polymeric compounds (PPh4)2{Rh2(μ–O2CR)4[Au(CN)2]2} (R = CMe3 (6), C6H4–p–CMe3 (7)). The structural characterization of 1, 3·2CH2Cl2, 4·3CH2Cl2, 5, 6, and 7·2OCMe2 is also provided with a detailed description of their crystal structures and intermolecular interactions. The polymeric compounds 3·2CH2Cl2, 4·3CH2Cl2, and 5 show wavy chains with Rh–Au–Rh and Rh–N–C angles in the ranges 177.18°–178.69° and 163.0°–170.4°, respectively. A comparative study with related rhodium-silver complexes previously reported indicates no significant influence of the gold or silver atoms in the solid-state arrangement of these kinds of complexes.
Integrating the Concept of Industry 4.0 by Teaching Methodology in Industrial Engineering Curriculum
Authors: Salah Bashir , Khan Sajjad , Ramadan Muawia , Gjeldum Nikola
Published: August 2020

The movement to digitally transform Saudi Arabia in all sectors has already begun under the “Vision 2030” program. Consequently, renovating and standardizing production and manufacturing industries to compete with global challenges is essential. The fourth industrial revolution (Industry 4.0) triggered by the development of information and communications technologies (ICT) provides a baseline for smart automation, using decentralized control and smart connectivity (e.g., Internet of Things). Industrial engineering graduates need to have acquaintance with this industrial digital revolution. Several industries where the spirit of Industry 4.0 has been embraced and have already implemented these ideas yielded gains. In this paper, a roadmap containing an academic term course based on the concept of Industry 4.0, which our engineering graduates passed through, is presented. At first, an orientation program to students elaborating on the Industry 4.0 concept, its main pillars, the importance of event-driven execution, and smart product manufacturing techniques. Then, various tasks in developing a learning factory were split and assigned among student groups. Finally, the evaluation of student potential in incorporating the Industry 4.0 concept was analyzed. This methodology led to their professional skill development and promoted students’ innovative ideas for the manufacturing sector.
Accumulation of Airborne Toxic Elements and Photosynthetic Performance of Lolium multiflorum L. Leaves
Authors: Borowiak Klaudia , Budka Anna , Lisiak-Zielińska Marta , Hanć Anetta , Zbierska Janina , Barałkiewicz Danuta , Kayzer Dariusz , Gaj Renata , Szymczak-Graczyk Anna , Kanclerz Jolanta
Published: August 2020

In this study, we aimed to investigate the accumulation of airborne trace elements in Lolium multiflorum leaves concerning photosynthetic activity parameters. Five sites for four 28-day series of plant exposition were selected. The concentration of trace elements in leaves was measured after each series, while photosynthetic activity parameters were measured three times during each series. Net photosynthesis rate (PN) and stomatal conductance (gs) were mostly negatively associated with all analyzed trace elements, unlike to CO2 concentrations (Ci). Arsenic was found with opposite trend in two exposure series. The high accumulation of Cd and Pb in plants recorded at two sites was mostly related to lowest PN and gs. Similar tendency for PN was found at sites and series with the highest Cr and Ni content in plants. L. multiflorum revealed a medium-level accumulation of trace elements and a low tolerance of the photosynthetic process to the presence of trace elements in ambient air.
An Integrated Method for Identifying Present Status and Risk of Drought in Bangladesh
Authors: Sarker Md. , Janet Nichol , Mansor Siti , Ahmad Baharin , Shahid Shamsuddin , Chung Eun-Sung , Reid Jeffrey , Siswanto Eko
Published: August 2020

The occurrence and severity of agricultural droughts may not be dependent upon climatic variables alone. Rather increasingly, drought is affected by human interventions such as irrigation. Anthropogenic activity has introduced uncertainty in the assessment of current drought and future drought risk in many parts of the world; neither climatic nor remote sensing data alone are able to assess drought conditions effectively. In response, we present a simple approach to assess drought by combining a remote sensing-based drought index, the Temperature Vegetation Dryness Index (TVDI), climate data (i.e., rainfall and temperature), and field observations to evaluate recent drought conditions in northwestern Bangladesh (NWB). Applying this approach, we gained five insights: (i) the TVDI successfully indicated the drought conditions of NWB and agrees with field observations, (ii) the integrated use of TVDI and climate data (such as rainfall and temperature) provides the best understanding of the difference between meteorological drought and droughts resulting from surface moisture conditions, (iii) the TVDI results agree with rainfall data (r2 = 0.40 in March and r2 = 46 in April) in a part of the study area (NWB) where irrigation is not available, (iv) the TVDI can be used along with climate data to predict the potential risk of drought, and (v) while meteorological drought exists due to low rainfall and high temperature in this NWB in pre-monsoon season, because of widespread irrigation practices, meteorological drought is unable to trigger agricultural drought over most parts of the study area. The findings imply that there is a potential risk of drought in NWB, since any disruption of irrigation water supply could trigger a severe agricultural drought over the whole region. This is similar to what is currently observed over a small part of NWB.
A Warming Mediterranean: 38 Years of Increasing Sea Surface Temperature
Authors: Pastor Francisco , Valiente Jose Antonio , Khodayar Samiro
Published: August 2020

The Mediterranean basin has been classified as a hot-spot for climate change. The Mediterranean Sea plays a fundamental regulatory role in the regional climate. We have analyzed the largest available and complete time series (1982–2019) of blended sea surface temperature (SST) data to study its seasonal cycle and look for a possible warming trend in the basin. From the analysis of the Mediterranean mean SST time series, a new temporal seasonal division is derived that differs from the one used in atmospheric climatology. Then, the SST time series were decomposed into their seasonal and trend components, and a consistent warming trend of 0.035 °C/year was obtained. The nature of this trend has been investigated, indicating a higher warming trend for both maximum and high/summer SST values than for the winter/colder ones. This reinforces the consistency of the SST increase since it is not only based on the presence of extreme values, but on a homogeneous basin global increase of high SST records as well. Although warming is found throughout the Mediterranean basin, the spatial variability found leads to the division of the basin into three distinct subareas regarding warming.
A New Hybrid Firefly–PSO Optimized Random Subspace Tree Intelligence for Torrential Rainfall-Induced Flash Flood Susceptible Mapping
Authors: Nhu Viet-Ha , Thi Ngo Phuong-Thao , Pham Tien Dat , Dou Jie , Song Xuan , Hoang Nhat-Duc , Tran Dang An , Cao Duong Phan , Aydilek İbrahim Berkan , Amiri Mahdis , Costache Romulus , Hoa Pham Viet , Tien Bui Dieu
Published: August 2020

Flash flood is one of the most dangerous natural phenomena because of its high magnitudes and sudden occurrence, resulting in huge damages for people and properties. Our work aims to propose a state-of-the-art model for susceptibility mapping of the flash flood using the decision tree random subspace ensemble optimized by hybrid firefly–particle swarm optimization (HFPS), namely the HFPS-RSTree model. In this work, we used data from a flood inventory map consisting of 1866 polygons derived from Sentinel-1 C-band synthetic aperture radar (SAR) data and a field survey conducted in the northwest mountainous area of the Van Ban district, Lao Cai Province in Vietnam. A total of eleven flooding conditioning factors (soil type, geology, rainfall, river density, elevation, slope, aspect, topographic wetness index (TWI), normalized difference vegetation index (NDVI), plant curvature, and profile curvature) were used as explanatory variables. These indicators were compiled from a geological and mineral resources map, soil type map, and topographic map, ALOS PALSAR DEM 30 m, and Landsat-8 imagery. The HFPS-RSTree model was trained and verified using the inventory map and the eleven conditioning variables and then compared with four machine learning algorithms, i.e., the support vector machine (SVM), the random forests (RF), the C4.5 decision trees (C4.5 DT), and the logistic model trees (LMT) models. We employed a range of statistical standard metrics to assess the predictive performance of the proposed model. The results show that the HFPS-RSTree model had the best predictive performance and achieved better results than those of other benchmarks with the ability to predict flash flood, reaching an overall accuracy of over 90%. It can be concluded that the proposed approach provides new insights into flash flood prediction in mountainous regions.
Integrated Approach to Investigating Historic Cemeteries
Authors: Downs Christine , Rogers Jaime , Collins Lori , Doering Travis
Published: August 2020

Ground-penetrating radar (GPR) and terrestrial laser scanning (TLS) surveys were conducted at a historic cemetery at Cape Canaveral Air Force Station, Florida, U.S., in order to confirm the presence of burials corresponding to grave markers and detect potential unmarked burials. Noise in the GPR data from surface features and subtle terrain differences must be addressed to determine the extent of anomalies of interest. We use singular value decomposition (SVD) to isolate and remove energy from GPR data. SVD allows one to remove unwanted signals that traditional processing techniques cannot. With SVD filtering, we resolve an anomaly adjacent to confirmed burials otherwise overprinted by unwanted signal. The migration of SVD-filtered data produces more distinct, spatially constrained point reflectors. Ground elevation is derived from georeferenced TLS data and compared to that from airborne laser scanning (ALS) to highlight subtle terrain that can assist data interpretation. TLS elevations show a subtle modern mound over the burial plot where ALS elevations show a depression. The targets of interest are approximately 20–30 cm higher in elevation if a topographic correction is performed using TLS versus ALS. In archaeological applications, a notable change is often recorded at the sub-meter scale. The combined approach presented here better resolves geophysical response of buried features and their positions in the ground relative to each other.
Estimating Land Surface Temperature from Satellite Passive Microwave Observations with the Traditional Neural Network, Deep Belief Network, and Convolutional Neural Network
Authors: Wang Shaofei , Zhou Ji , Lei Tianjie , Wu Hua , Zhang Xiaodong , Ma Jin , Zhong Hailing
Published: August 2020

Neural networks, especially the latest deep learning, have exhibited good ability in estimating surface parameters from satellite remote sensing. However, thorough examinations of neural networks in the estimation of land surface temperature (LST) from satellite passive microwave (MW) observations are still lacking. Here, we examined the performances of the traditional neural network (NN), deep belief network (DBN), and convolutional neural network (CNN) in estimating LST from the AMSR-E and AMSR2 data over the Chinese landmass. The examinations were based on the same training set, validation set, and test set extracted from 2003, 2004, and 2009, respectively, for AMSR-E with a spatial resolution of 0.25°. For AMSR2, the three sets were extracted from 2013, 2014, and 2016 with a spatial resolution of 0.1°, respectively. MODIS LST played the role of “ground truth” in the training, validation, and testing. The examination results show that CNN is better than NN and DBN by 0.1–0.4 K. Different combinations of input parameters were examined to get the best combinations for the daytime and nighttime conditions. The best combinations are the brightness temperatures (BTs), NDVI, air temperature, and day of the year (DOY) for the daytime and BTs and air temperature for the nighttime. By adding three and one easily obtained parameters on the basis of BTs, the accuracies of LST estimates can be improved by 0.8 K and 0.3 K for the daytime and nighttime conditions, respectively. Compared with the MODIS LST, the CNN LST estimates yielded root-mean-square differences (RMSDs) of 2.19–3.58 K for the daytime and 1.43–2.14 K for the nighttime for diverse land cover types for AMSR-E. Validation against the in-situ LSTs showed that the CNN LSTs yielded root-mean-square errors of 2.10–4.72 K for forest and cropland sites. Further intercomparison indicated that ~50% of the CNN LSTs were closer to the MODIS LSTs than ESA’s GlobTemperature AMSR-E LSTs, and the average RMSDs of the CNN LSTs were less than 3 K over dense vegetation compared to NASA’s global land parameter data record air temperatures. This study helps better the understanding of the use of neural networks for estimating LST from satellite MW observations.
Fishpond Mapping by Spectral and Spatial-Based Filtering on Google Earth Engine: A Case Study in Singra Upazila of Bangladesh
Authors: Yu Zhiqi , Di Liping , Rahman Md. Shahinoor , Tang Junmei
Published: August 2020

Inland aquaculture in Bangladesh has been growing fast in the last decade. The underlying land use/land cover (LULC) change is an important indicator of socioeconomic and food structure change in Bangladesh, and fishpond mapping is essential to understand such LULC change. Previous research often used water indexes (WI), such as Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI), to enhance water bodies and use shape-based metrics to assist classification of individual water features, such as coastal aquaculture ponds. However, inland fishponds in Bangladesh are generally extremely small, and little research has investigated mapping of such small water objects without high-resolution images. Thus, this research aimed to bridge the knowledge gap by developing and evaluating an automatic fishpond mapping workflow with Sentinel-2 images that is implemented on Google Earth Engine (GEE) platform. The workflow mainly includes two steps: (1) the spectral filtering phase that uses a pixel selection technique and an image segmentation method to automatically identify all-year-inundated water bodies and (2) spatial filtering phase to further classify all-year-inundated water bodies into fishponds and non-fishponds using object-based features (OBF). To evaluate the performance of the workflow, we conducted a case study in the Singra Upazila of Bangladesh, and our method can efficiently map inland fishponds with a precision score of 0.788. Our results also show that the pixel selection technique is essential in identifying inland fishponds that are generally small. As the workflow is implemented on GEE, it can be conveniently applied to other regions.
Long-Term Discharge Estimation for the Lower Mississippi River Using Satellite Altimetry and Remote Sensing Images
Authors: Scherer Daniel , Schwatke Christian , Dettmering Denise , Seitz Florian
Published: August 2020

Despite increasing interest in monitoring the global water cycle, the availability of in situ gauging and discharge time series is decreasing. However, this lack of ground data can partly be compensated for by using remote sensing techniques to observe river stages and discharge. In this paper, a new approach for estimating discharge by combining water levels from multi-mission satellite altimetry and surface area extents from optical imagery with physical flow equations at a single cross-section is presented and tested at the Lower Mississippi River. The datasets are combined by fitting a hypsometric curve, which is then used to derive the water level for each acquisition epoch of the long-term multi-spectral remote sensing missions. In this way, the chance of detecting water level extremes is increased and a bathymetry can be estimated from water surface extent observations. Below the minimum hypsometric water level, the river bed elevation is estimated using an empirical width-to-depth relationship in order to determine the final cross-sectional geometry. The required flow gradient is derived from the differences between virtual station elevations, which are computed in a least square adjustment from the height differences of all multi-mission satellite altimetry data that are close in time. Using the virtual station elevations, satellite altimetry data from multiple virtual stations and missions are combined to one long-term water level time series. All required parameters are estimated purely based on remote sensing data, without using any ground data or calibration. The validation at three gauging stations of the Lower Mississippi River shows large deviations primarily caused by the below average width of the predefined cross-sections. At 13 additional cross-sections situated in wide, uniform, and straight river sections nearby the gauges the Normalized Root Mean Square Error (NRMSE) varies between 10.95% and 28.43%. The Nash-Sutcliffe Efficiency (NSE) for these targets is in a range from 0.658 to 0.946.
Prediction of Sea Surface Temperature in the China Seas Based on Long Short-Term Memory Neural Networks
Authors: Wei Li , Guan Lei , Qu Liqin , Guo Dongsheng
Published: August 2020

Sea surface temperature (SST) in the China Seas has shown an enhanced response in the accelerated global warming period and the hiatus period, causing local climate changes and affecting the health of coastal marine ecological systems. Therefore, SST distribution prediction in this area, especially seasonal and yearly predictions, could provide information to help understand and assess the future consequences of SST changes. The past few years have witnessed the applications and achievements of neural network technology in SST prediction. Due to the diversity of SST features in the China Seas, long-term and high-spatial-resolution prediction remains a crucial challenge. In this study, we adopted long short-term memory (LSTM)-based deep neural networks for 12-month lead time SST prediction from 2015 to 2018 at a 0.05° spatial resolution. Considering the sub-regional differences in the SST features of the study area, we applied self-organizing feature maps (SOM) to classify the SST data first, and then used the classification results as additional inputs for model training and validation. We selected nine models differing in structure and initial parameters for ensemble to overcome the high variance in the output. The statistics of four years’ SST difference between the predicted SST and Operational SST and Ice Analysis (OSTIA) data shows the average root mean square error (RMSE) is 0.5 °C for a one-month lead time and is 0.66 °C for a 12-month lead time. The southeast of the study area shows the highest predictable accuracy, with an RMSE less than 0.4 °C for a 12-month prediction lead time. The results indicate that our model is feasible and provides accurate long-term and high-spatial-resolution SST prediction. The experiments prove that introducing appropriate class labels as auxiliary information can improve the prediction accuracy, and integrating models with different structures and parameters can increase the stability of the prediction results.
ISAR Image Matching and Three-Dimensional Scattering Imaging Based on Extracted Dominant Scatterers
Authors: Xu Dan , Bie Bowen , Sun Guang-Cai , Xing Mengdao , Pascazio Vito
Published: August 2020

This paper studies inverse synthetic aperture radar (ISAR) image matching and three-dimensional (3D) scattering imaging based on extracted dominant scatterers. In the condition of a long baseline between two radars, it is easy for obvious rotation, scale, distortion, and shift to occur between two-dimensional (2D) radar images. These problems lead to the difficulty of radar-image matching, which cannot be resolved by motion compensation and cross-correlation. What is more, due to the anisotropy, existing image-matching algorithms, such as scale invariant feature transform (SIFT), do not adapt to ISAR images very well. In addition, the angle between the target rotation axis and the radar line of sight (LOS) cannot be neglected. If so, the calibration result will be smaller than the real projection size. Furthermore, this angle cannot be estimated by monostatic radar. Therefore, instead of matching image by image, this paper proposes a novel ISAR imaging matching and 3D imaging based on extracted scatterers to deal with these issues. First, taking advantage of ISAR image sparsity, radar images are converted into scattering point sets. Then, a coarse scatterer matching based on the random sampling consistency algorithm (RANSAC) is performed. The scatterer height and accurate affine transformation parameters are estimated iteratively. Based on matched scatterers, information such as the angle and 3D image can be obtained. Finally, experiments based on the electromagnetic simulation software CADFEKO have been conducted to demonstrate the effectiveness of the proposed algorithm.
Protein Determination with Molecularly Imprinted Polymer Recognition Combined with Birefringence Liquid Crystal Detection
Authors: Cieplak Maciej , Węgłowski Rafał , Iskierko Zofia , Węgłowska Dorota , Sharma Piyush S. , Noworyta Krzysztof R. , D’Souza Francis , Kutner Wlodzimierz
Published: August 2020

Liquid crystal-based sensors offer the advantage of high sensitivity at a low cost. However, they often lack selectivity altogether or require costly and unstable biomaterials to impart this selectivity. To incur this selectivity, we herein integrated a molecularly imprinted polymer (MIP) film recognition unit with a liquid crystal (LC) in an optical cell transducer. We tested the resulting chemosensor for protein determination. We examined two different LCs, each with a different optical birefringence. That way, we revealed the influence of that parameter on the sensitivity of the (human serum albumin)-templated (MIP-HSA) LC chemosensor. The response of this chemosensor with the (MIP-HSA)-recognizing film was linear from 2.2 to 15.2 µM HSA, with a limit of detection of 2.2 µM. These values are sufficient to use the devised chemosensor for HSA determination in biological samples. Importantly, the imprinting factor (IF) of this chemosensor was appreciable, reaching IF = 3.7. This IF value indicated the predominant binding of the HSA through specific rather than nonspecific interactions with the MIP.
Artificial Intelligence-Driven Oncology Clinical Decision Support System for Multidisciplinary Teams
Authors: Lee Kyounga , Lee Seon Heui
Published: August 2020

Watson for Oncology (WfO) is a clinical decision support system driven by artificial intelligence. In Korea, WfO is used by multidisciplinary teams (MDTs) caring for cancer patients. This study aimed to investigate the effect of WfO use on hospital satisfaction and perception among patients cared for by MDTs. This was a descriptive study that used a written survey to gather information from cancer patients at a hospital in Korea. The rate of positive change in patient perception after treatment was 86.8% in the MDT-WfO group and 71.2% in the MDT group. In terms of easily understandable explanations, the MDT-WfO (9.53 points) group reported higher satisfaction than the MDT group (9.24 points). Younger patients in the MDT-WfO group showed high levels of satisfaction and reliability of treatment. When WfO was used, the probability of positive change in patient perception of the hospital was 2.53 times higher than when WfO was not used. With a one-point increase in overall satisfaction, the probability of positive change in patient perception of the hospital increased 1.97 times. Therefore, if WfO is used appropriately in the medical field, it may enhance patient satisfaction and change patient perception positively.
A Decoupled Unified Observation Method of Stochastic Multidimensional Vibration for Wind Tunnel Models
Authors: Zhou Mengde , Liu Wei , Wang Qinqin , Liang Bing , Tang Linlin , Zhang Yang , Cui Xiaochun
Published: August 2020

Active vibration control is the most effective method for stochastic multidimensional vibration in wind tunnel tests, in which vibration monitoring is the core foundation. Vibrations are induced by the disturbances of several complex air flow instabilities under extreme test conditions with high attack angles. Here, a decoupled unified observation method is proposed in order to fully monitor stochastic multidimensional vibration. First, stochastic multidimensional vibration is explained using the Cartesian coordinate system. Then, the multidimensional vibration decoupling of the pitch plane and the yaw plane is realized according to the proposed decoupling design principle of the long cantilever sting. A unified observation method is presented, based on inertial force theory, to observe multidimensional vibration due to acceleration in each decoupling plane. Verification experiments were conducted in lab and a transonic wind tunnel, using an established real-time monitoring system. The results of lab experiments indicate that, in the frequency region of 0–120 Hz, three vibration modes of a selected stochastic vibration can be decoupled and observed through the vibration components in pitch plane and yaw plane. In addition, wind tunnel tests were carried out according to the working conditions (α = −4~10° with γ = 45°) at Ma = 0.6 and Ma = 0.7, respectively. The results show that six vibration modes of two selected stochastic vibrations can be decoupled and observed through the vibration components in pitch plane and yaw plane. The experimental results prove that stochastic vibration can be fully monitored in multiple dimensions through the vibration components in pitch plane and yaw plane using the proposed decoupled unified observation method. Therefore, these results lay the foundation for active vibration control.