bluuebunny/crossref_metadata_2025 huggingface dataset:
Opportunities and Challenges in Studies of Host-Pathogen Interactions and Management of Verticillium dahliae in Tomatoes
Authors: Acharya Bhupendra , Ingram Thomas W. , Oh YeonYee , Adhikari Tika B. , Dean Ralph A. , Louws Frank J.
Published: November 2020

Tomatoes (Solanum lycopersicum L.) are a valuable horticultural crop that are grown and consumed worldwide. Optimal production is hindered by several factors, among which Verticillium dahliae, the cause of Verticillium wilt, is considered a major biological constraint in temperate production regions. V. dahliae is difficult to mitigate because it is a vascular pathogen, has a broad host range and worldwide distribution, and can persist in soil for years. Understanding pathogen virulence and genetic diversity, host resistance, and plant-pathogen interactions could ultimately inform the development of integrated strategies to manage the disease. In recent years, considerable research has focused on providing new insights into these processes, as well as the development and integration of environment-friendly management approaches. Here, we discuss the current knowledge on the race and population structure of V. dahliae, including pathogenicity factors, host genes, proteins, enzymes involved in defense, and the emergent management strategies and future research directions for managing Verticillium wilt in tomatoes.
Functional Attributes and Anticancer Potentialities of Chico (Pachycereus Weberi) and Jiotilla (Escontria Chiotilla) Fruits Extract
Authors: Sandate-Flores Luisaldo , Romero-Esquivel Eduardo , Rodríguez-Rodríguez José , Rostro-Alanis Magdalena , Melchor-Martínez Elda M. , Castillo-Zacarías Carlos , Ontiveros Patricia Reyna , Celaya Marcos Fredy Morales , Chen Wei-Ning , Iqbal Hafiz M. N. , Parra-Saldívar Roberto
Published: November 2020

Mexico has a great diversity of cacti, however, many of their fruits have not been studied in greater depth. Several bioactive compounds available in cacti juices extract have demonstrated nutraceutical properties. Two cactus species are interesting for their biologically active pigments, which are chico (Pachycereus weberi (J. M. Coult.) Backeb)) and jiotilla (Escontria chiotilla (Weber) Rose)). Hence, the goal of this work was to evaluate the bioactive compounds, i.e., betalains, total phenolic, vitamin C, antioxidant, and mineral content in the extract of the above-mentioned P. weberi and E. chiotilla. Then, clarified extracts were evaluated for their antioxidant activity and cytotoxicity (cancer cell lines) potentialities. Based on the obtained results, Chico fruit extract was found to be a good source of vitamin C (27.19 ± 1.95 mg L-Ascorbic acid/100 g fresh sample). Moreover, chico extract resulted in a high concentration of micronutrients, i.e., potassium (517.75 ± 16.78 mg/100 g) and zinc (2.46 ± 0.65 mg/100 g). On the other hand, Jiotilla has a high content of biologically active pigment, i.e., betaxanthins (4.17 ± 0.35 mg/g dry sample). The antioxidant activities of clarified extracts of chico and jiotilla were 80.01 ± 5.10 and 280.88 ± 7.62 mg/100 g fresh sample (DPPH method), respectively. From the cytotoxicity perspective against cancer cell lines, i.e., CaCo-2, MCF-7, HepG2, and PC-3, the clarified extracts of chico showed cytotoxicity (%cell viability) in CaCo-2 (49.7 ± 0.01%) and MCF-7 (45.56 ± 0.05%). A normal fibroblast cell line (NIH/3T3) was used, as a control, for comparison purposes. While jiotilla extract had cytotoxicity against HepG2 (47.31 ± 0.03%) and PC-3 (53.65 ± 0.04%). These results demonstrated that Chico and jiotilla are excellent resources of biologically active constituents with nutraceuticals potentialities.
Enhancing Biosludge Dewaterability with Hemoglobin from Waste Blood as a Bioflocculant
Authors: Ghazisaidi Hamed , Garcia Rafael A. , Tran Honghi , Yuan Runlin , Allen D. Grant
Published: November 2020

Synthetic polymers are widely used in the treatment of biosludge (waste activated sludge) to enhance its dewaterability. This paper discusses the results of a systematic study using hemoglobin (Hb) from animal blood and methylated hemoglobin (MeHb), a derivative in which a methyl group replaces the hydrogen carboxyl groups, to replace synthetic polymers to improve the dewatering efficiency of biosludge. With regular hemoglobin, no improvement in biosludge dewatering was found. With 10% of methylated hemoglobin per total solids content, however, the dry solids content of biosludge increased from 10.2 (±0.3) wt% to 15.0 (±1.0) wt%. Zeta potential measurements showed a decrease in the negative surface charge of the particles in biosludge from −34.3 (±3.2) mV to −19.0 (±2.1) mV after the treatment with methylated hemoglobin. This, along with an unchanged particle size distribution after conditioning, suggests that charge neutralization is likely the main cause of particle flocculation. With charges neutralized, the extracellular polymeric substances (EPS) around the biosludge flocs become loose, releasing the trapped water, thus increasing dewaterability.
Surface Chemistry of Polymers
Authors: Vesel Alenka
Published: November 2020

Surface chemistry plays a key role in modern applications of polymer materials [...]
Crosslinked Pore-Filling Anion Exchange Membrane Using the Cylindrical Centrifugal Force for Anion Exchange Membrane Fuel Cell System
Authors: Son Tae , Kim Tae-Hyun , Nam Sang
Published: November 2020

In this study, novel crosslinked pore-filling membranes were fabricated by using a centrifugal force from the cylindrical centrifugal machine. For preparing these crosslinked pore-filling membranes, the poly(phenylene oxide) containing long side chains to improve the water management (hydrophilic), porous polyethylene support (hydrophobic) and crosslinker based on the diamine were used. The resulting membranes showed a uniform thickness, flexible and transparent because it is well filled. Among them, PF-XAc-PPO70_25 showed good mechanical properties (56.1 MPa of tensile strength and 781.0 MPa of Young’s modulus) and dimensional stability due to the support. In addition, it has a high hydroxide conductivity (87.1 mS/cm at 80 °C) and low area specific resistance (0.040 Ω·cm2), at the same time showing stable alkaline stability. These data outperformed the commercial FAA-3-50 membrane sold by Fumatech in Germany. Based on the optimized properties, membrane electrode assembly using XAc-PPO70_25 revealed excellent cell performance (maximum power density: 239 mW/cm2 at 0.49 V) than those of commercial FAA-3-50 Fumatech anion exchange membrane (maximum power density: 212 mW/cm2 at 0.54 V) under the operating condition of 60 °C and 100% RH as well. It was expected that PF-XAc-PPO70_25 could be an excellent candidate based on the results superior to those of commercial membranes in these essential characteristics of fuel cells.
Cross-Linking Modification of Ammonium Polyphosphate via Ionic Exchange and Self-Assembly for Enhancing the Fire Safety Properties of Polypropylene
Authors: Pan Yingtong , Luo Zhonglin , Wang Biaobing
Published: November 2020

Modified ammonium polyphosphate (MAPP) was prepared as a novel mono-component intumescent flame retardant (IFR) via the ionic exchange between ammonium polyphosphate (APP) and piperazine sulfonate, which is synthesized by self-assembly using 1-(2-aminoethyl) piperazine (AEP) and p-aminobenzene sulfonic acid (ASC) as raw materials. This all-in-one IFR integrating three functional elements (carbon, acid, and gas source) showed more efficient flame retardancy and excellent smoke suppression as well as better mechanical properties than the conventional APP. The incorporation of 22.5 wt.% MAPP into polypropylene (PP) eliminated the melt dripping phenomenon and passed the UL-94 V-0 rating. The results of the cone calorimetry test (CCT) revealed that the release of heat, smoke, and CO is significantly decreased, demonstrating that this novel IFR endows PP with excellent fire safety more effectively. For PP/MAPP composites, a possible IFR mechanism was proposed based on the analysis of the pyrolysis gas and char residues.
Application of a Modeling Tool to Describe Fly Ash Generation, Composition, and Melting Behavior in a Wheat Straw Fired Commercial Power Plant
Authors: Funcia Ibai , Bimbela Fernando , Gil Javier , Gandía Luis M.
Published: November 2020

Ash behavior is a key operational aspect of industrial-scale power generation by means of biomass combustion. In this work, FactSageTM 6.4 software was used to develop and assess three models of wheat straw combustion in a vibrating grate-fired commercial boiler of 16 MWth, aiming to describe the inorganic elements release as well as fly ash melting behavior and composition. Simulations were carried out solving four consecutive calculation stages corresponding to the main plant sections. Chemical fractionation was adopted in order to distinguish between reactive, inert and partially reactive biomass fractions. The developed models allow take into account different levels of partial reactivity, values of the temperature for each sub-stage on the grate, and ways to apply entrained streams based on data from the elemental analyses of the fly ashes. To this end, two one-week experimental campaigns were conducted in the plant to carry out the sampling. It has been found that considering chemical fractionation is indispensable to describe the entrainment of solid particles in the gas stream. In addition, the best results are obtained by adopting a small reactivity (2%) of the inert fraction. As for fly ash composition, the concentrations of the major elements showed good agreement with the results from the chemical analyses. In the case of S and Cl, calculations revealed a match with gas cooling effects in the superheaters as well as an entrainment effect. The melting behavior together with the presence of KCl and K2SO4 condensates, point out at possible corrosion phenomena in walls at temperatures of 700–750 °C.
Digital Twinning Process for Stirred Tank Reactors/Separation Unit Operations through Tandem Experimental/Computational Fluid Dynamics (CFD) Simulations
Authors: Oblak Blaž , Babnik Simon , Erklavec-Zajec Vivian , Likozar Blaž , Pohar Andrej
Published: November 2020

Computational fluid dynamics simulations (CFD) were used to evaluate mixing in baffled and unbaffled vessels. The Reynolds-averaged Navier−Stokes k–ε model was implemented in OpenFOAM for obtaining the fluid flow field. The 95% homogenization times were determined by tracer tests. Experimental tests were conducted by injecting sodium chloride into the vessel and measuring the conductivity with two conductivity probes, while the simulations replicated the experimental conditions with the calculation of the transport of species. It was found that the geometry of the system had a great effect on the mixing time, since the irregular flow distribution, which can be obtained with baffles, can lead to local stagnation zones, which will increase the time needed to achieve the homogenization of the solute. It was also found that measuring local, pointwise concentrations can lead to a high underestimation of the global mixing time required for the homogenization of the entire vessel. Dissolution of sucrose was also studied experimentally and by mathematical modeling. The dissolution of sucrose was found to be kinetically limited and a very good agreement was found between the experiments and the modeling approach. The extent of the applicability of CFD simulations was evaluated for enabling rapid process design via simulations.
Production of Protein Hydrolysate Containing Antioxidant and Angiotensin -I-Converting Enzyme (ACE) Inhibitory Activities from Tuna (Katsuwonus pelamis) Blood
Authors: Mongkonkamthorn Natthaphon , Malila Yuwares , Yarnpakdee Suthasinee , Makkhun Sakunkhun , Regenstein Joe M. , Wangtueai Sutee
Published: November 2020

Tuna blood (TB) was subjected to enzymatic hydrolysis. The effects of the relationship of hydrolysis time (30–180 min) and enzyme concentration (0.5–3.0% w/w protein) on the degree of hydrolysis (DH), yield, antioxidant and angiotensin-I-converting enzyme (ACE) inhibitory activities were determined. The response surface methodology (RSM) showed that TB hydrolysis’s optimum conditions were hydrolysis for 180 min and Alcalase, Neutrase or Flavourzyme at 2.81%, 2.89% or 2.87% w/w protein, respectively. The hydrolysates with good DH (40–46%), yield (3.5–4.6%), the IC50 of DPPH (0.8–1.6 mg/mL) and ABTS (1.0–1.4 mg/mL) radical scavenging activity, ferric reducing antioxidant power (FRAP) value (0.28–0.65 mmol FeSO4/g) and IC50 of ACE inhibitory activity (0.15–0.28 mg/mL) were obtained with those conditions. The TB hydrolysate using Neutrase (TBHN) was selected for characterization in terms of amino acid composition, peptide fractions and sensory properties. The essential, hydrophobic and hydrophilic amino acids in TBHN were ~40%, 60% and 20% of total amino acids, respectively. The fraction of molecular weight <1 kDa showed the highest antioxidant and ACE inhibitory activities. Fishiness and bitterness were the main sensory properties of TBHN. Fortification of TBHN in mango jelly at ≤ 0.5% (w/w) was accepted by consumers as like moderately to like slightly, while mango jelly showed strong antioxidant and ACE inhibitory activities. TBHN could be developed for natural antioxidants and antihypertensive peptides in food and functional products.
Measurement of Inter-Individual Variability in Assessing the Quality of Life in Respondents with Celiac Disease
Authors: Bacci Silvia , Caso Daniela , Fabbricatore Rosa , Iannario Maria
Published: November 2020

Quality of life of Celiac Disease (CD) patients is affected by constraints in their physical, social and emotional behaviour. Our objective is to assess differences in two relevant dimensions of the Celiac Quality of Life (CQoL) scale, Limitations due to the disease and Dysphoria (i.e., feelings of depression and discomfort), in relation to the perceived social support and some individual and disease-related characteristics. The paper exploits suitable unidimensional Item Response Theory (IRT) models to individually analyse the two mentioned dimensions of the CQoL and Multidimensional Latent Class IRT models for ordinal polytomous items in order to detect sub-populations of CD patients that are homogenous with respect to the perceived CQoL. The latter methods allow to address patients with similar characteristics to the same treatment, performing at the same time a more tailored overture to health promotion programmes. The analysis extracts the relevant patterns and relations among CD patients, disentangling respondents receiving CD diagnosis in adolescence or adult age rather than in childhood (the first perceive high levels of Limitations and Dysphoria), patients with high perceived social support, a factor influencing in a positive way motivation to engage in management of CD-related distress and psychological well-being, and participants who are married or cohabiting. The latter report higher latent trait levels.
Transformation of the Personnel Training System for Oil and Gas Projects in the Russian Arctic
Authors: Samylovskaya Ekaterina , Kudryavtseva Regina-Elizaveta , Medvedev Dmitriy , Grinyaev Sergey , Nordmann Alfred
Published: November 2020

This paper analyses the process of transforming specialist training systems for oil and gas projects in the Arctic, which has been taking place within the structure of education in Russia over the past decade. Using classical methods of analysis, synthesis, and classification, the authors studied the main global trends in training personnel for the Arctic and the manifestations of these trends in the system of training Russian specialists. To identify the qualitative characteristics of the educational system development, the authors applied the survey method and composed a list of leading universities in training personnel for the Russian Arctic, as well as the “Arctic professions of the future”. As a result of the study, the authors came to the conclusion that global trends in training “Arctic personnel” show the need to develop an interdisciplinary approach, to form basic knowledge in Natural Sciences, to study the socio-cultural specifics of the region, to develop new educational standards, to implement the concept of ‘Life Long Learning’, to widely introduce digital technologies and to internationalize education. In general, the Russian personnel training system is adapting to changing conditions, in particular, some progress has been made in the formation of “digital” competencies and skills to work in a developed IT infrastructure. The introduction of “digital fields” has led to an increase in the demand for IT specialists in the Arctic oil and gas sector. With the help of an expert survey, it was revealed that in the future, the most popular professions, along with “drillers” and transport specialists, will be IT specialists who ensure the “digital fields” functioning. The leading Russian universities that train specialists for modern oil and gas projects in the Arctic have been identified. It is noted that not all leading industry universities in Russia are participating in international educational projects and organizations. There is skepticism about the internationalization of education.
Functional Design of a 6-DOF Platform for Micro-Positioning
Authors: Palpacelli Matteo-Claudio , Carbonari Luca , Palmieri Giacomo , D’Anca Fabio , Landini Ettore , Giorgi Guido
Published: November 2020

Parallel kinematic machines (PKMs) have demonstrated their potential in many applications when high stiffness and accuracy are needed, even at micro- and nanoscales. The present paper is focused on the functional design of a parallel platform providing high accuracy and repeatability in full spatial motion. The hexaglide architecture with 6-PSS kinematics was demonstrated as the best solution according to the specifications provided by an important Italian company active in the field of micro-positioning, particularly in vacuum applications. All the steps needed to prove the applicability of such kinematics at the microscale and their inherent advantages are presented. First, the kinematic model of the manipulator based on the study’s parametrization is provided. A global conditioning index (GCI) is proposed in order to optimize the kinetostatic performance of the robot, so that precise positioning in the required platform workspace is guaranteed avoiding singular configurations. Some numerical simulations demonstrate the effectiveness of the study. Finally, some details about the realization of a physical prototype are given.
UAV-Derived Data Application for Environmental Monitoring of the Coastal Area of Lake Sevan, Armenia with a Changing Water Level
Authors: Medvedev Andrey , Telnova Natalia , Alekseenko Natalia , Koshkarev Alexander , Kuznetchenko Pyotr , Asmaryan Shushanik , Narykov Alexey
Published: November 2020

The paper presents the range and applications of thematic tasks for ultra-high spatial resolution data from small unmanned aerial vehicles (UAVs) in the integral system of environmental multi-platform and multi-scaled monitoring of Lake Sevan, which is one of the greatest freshwater lakes in Eurasia. From the 1930s, it had been subjected to human-driven changing of the water level with associated and currently exacerbated environmental issues. We elaborated the specific techniques of optical and thermal surveys for the different coastal sites and phenomena in study. UAV-derived optical imagery and thermal stream were processed by a Structure-from-Motion algorithm to create digital surface models (DSMs) and ortho-imagery for several key sites. UAV imagery were used as additional sources of detailed spatial data under large-scale mapping of current land-use and point sources of water pollution in the coastal zone, and a main data source on environmental violations, especially sewage discharge or illegal landfills. The revealed present-day coastal types were mapped at a large scale, and the net changes of shoreline position and rates of shore erosion were calculated on multi-temporal UAV data using modified Hausdorff’s distance. Based on highly-detailed DSMs, we revealed the areas and objects at risk of flooding under the projected water level rise to 1903.5 m along the west coasts of Minor Sevan being the most popular recreational area. We indicated that the structural and environmental state of marsh coasts and coastal wetlands as potential sources of lake eutrophication and associated algal blooms could be more efficiently studied under thermal UAV surveys than optical ones. We proposed to consider UAV surveys as a necessary intermediary between ground data and satellite imagery with different spatial resolutions for the complex environmental monitoring of the coastal area and water body of Lake Sevan as a whole.
An Automatic Tree Skeleton Extraction Approach Based on Multi-View Slicing Using Terrestrial LiDAR Scans Data
Authors: Ai Mingyao , Yao Yuan , Hu Qingwu , Wang Yue , Wang Wei
Published: November 2020

Effective 3D tree reconstruction based on point clouds from terrestrial Light Detection and Ranging (LiDAR) scans (TLS) has been widely recognized as a critical technology in forestry and ecology modeling. The major advantages of using TLS lie in its rapidly and automatically capturing tree information at millimeter level, providing massive high-density data. In addition, TLS 3D tree reconstruction allows for occlusions and complex structures from the derived point cloud of trees to be obtained. In this paper, an automatic tree skeleton extraction approach based on multi-view slicing is proposed to improve the TLS 3D tree reconstruction, which borrowed the idea from the medical imaging technology of X-ray computed tomography. Firstly, we extracted the precise trunk center and then cut the point cloud of the tree into slices. Next, the skeleton from each slice was generated using the kernel mean shift and principal component analysis algorithms. Accordingly, these isolated skeletons were smoothed and morphologically synthetized. Finally, the validation in point clouds of two trees acquired from multi-view TLS further demonstrated the potential of the proposed framework in efficiently dealing with TLS point cloud data.
Applying Deep Learning to Clear-Sky Radiance Simulation for VIIRS with Community Radiative Transfer Model—Part 2: Model Architecture and Assessment
Authors: Liang Xingming , Liu Quanhua
Published: November 2020

A fully connected “deep” neural network algorithm with the Community Radiative Transfer Model (FCDN_CRTM) is proposed to explore the efficiency and accuracy of reproducing the Visible Infrared Imaging Radiometer Suite (VIIRS) radiances in five thermal emission M (TEB/M) bands. The model was trained and tested in the nighttime global ocean clear-sky domain, in which the VIIRS observation minus CRTM (O-M) biases have been well validated in recent years. The atmosphere profile from the European Centre for Medium-Range Weather Forecasts (ECMWF) and sea surface temperature (SST) from the Canadian Meteorology Centre (CMC) were used as FCDN_CRTM input, and the CRTM-simulated brightness temperatures (BTs) were defined as labels. Six dispersion days’ data from 2019 to 2020 were selected to train the FCDN_CRTM, and the clear-sky pixels were identified by an enhanced FCDN clear-sky mask (FCDN_CSM) model, which was demonstrated in Part 1. The trained model was then employed to predict CRTM BTs, which were further validated with the CRTM BTs and the VIIRS sensor data record (SDR) for both efficiency and accuracy. With iterative refinement of the model design and careful treatment of the input data, the agreement between the FCDN_CRTM and the CRTM was generally good, including the satellite zenith angle and column water vapor dependencies. The mean biases of the FCDN_CRTM minus CRTM (F-C) were typically ~0.01 K for all five bands, and the high accuracy persisted during the whole analysis period. Moreover, the standard deviations (STDs) were generally less than 0.1 K and were consistent for approximately half a year, before they significantly degraded. The validation with VIIRS SDR data revealed that both the predicted mean biases and the STD of the VIIRS observation minus FCDN_CRTM (V-F) were comparable with the VIIRS minus direct CRTM simulation (V-C). Meanwhile, both V-F and V-C exhibited consistent global geophysical and statistical distribution, as well as stable long-term performance. Furthermore, the FCDN_CRTM processing time was more than 40 times faster than CRTM simulation. The highly efficient, accurate, and stable performances indicate that the FCDN_CRTM is a potential solution for global and real-time monitoring of sensor observation minus model simulation, particularly for high-resolution sensors.
UAV Laser Scans Allow Detection of Morphological Changes in Tree Canopy
Authors: Slavík Martin , Kuželka Karel , Modlinger Roman , Tomášková Ivana , Surový Peter
Published: November 2020

High-resolution laser scans from unmanned aerial vehicles (UAV) provide a highly detailed description of tree structure at the level of fine branches. Apart from ultrahigh spatial resolution, unmanned aerial laser scanning (ULS) can also provide high temporal resolution due to its operability and flexibility during data acquisition. We examined the phenomenon of bending branches of dead trees during one year from ULS multi-temporal data. In a multi-temporal series of three ULS datasets, we detected a synchronized reversible change in the inclination angles of the branches of 43 dead trees in a stand of blue spruce (Picea pungens Engelm.). The observed phenomenon has important consequences for both tree physiology and forest remote sensing (RS). First, the inclination angle of branches plays a crucial role in solar radiation interception and thus influences the total photosynthetic gain. The ability of a tree to change the branch position has important ecophysiological consequences, including better competitiveness across the site. Branch shifting in dead trees could be regarded as evidence of functional mycorrhizal interconnections via roots between live and dead trees. Second, we show that the detected movement results in a significant change in several point cloud metrics often utilized for deriving forest inventory parameters, both in the area-based approach (ABA) and individual tree detection approaches, which can affect the prediction of forest variables. To help quantify its impact, we used point cloud metrics of automatically segmented individual trees to build a generalized linear model to classify trees with and without the observed morphological changes. The model was applied to a validation set and correctly identified 86% of trees that displayed branch movement, as recorded by a human observer. The ULS allows for the study of this phenomenon across large areas, not only at individual tree levels.
Number of Building Stories Estimation from Monocular Satellite Image Using a Modified Mask R-CNN
Authors: Ji Chao , Tang Hong
Published: November 2020

Stereo photogrammetric survey used to be used to extract the height of buildings, then to convert the height to number of stories through certain rules to estimate the number of stories of buildings by means of satellite remote sensing. In contrast, we propose a new method using deep learning to estimate the number of stories of buildings from monocular optical satellite image end to end in this paper. To the best of our knowledge, this is the first attempt to directly estimate the number of stories of buildings from monocular satellite images. Specifically, in the proposed method, we extend a classic object detection network, i.e., Mask R-CNN, by adding a new head to predict the number of stories of detected buildings from satellite images. GF-2 images from nine cities in China are used to validate the effectiveness of the proposed methods. The result of experiment show that the mean absolute error of prediction on buildings whose stories between 1–7, 8–20, and above 20 are 1.329, 3.546, and 8.317, respectively, which indicate that our method has possible application potentials in low-rise buildings, but the accuracy in middle-rise and high-rise buildings needs to be further improved.
Probabilistic Mangrove Species Mapping with Multiple-Source Remote-Sensing Datasets Using Label Distribution Learning in Xuan Thuy National Park, Vietnam
Authors: Xia Junshi , Yokoya Naoto , Pham Tien Dat
Published: November 2020

Mangrove forests play an important role in maintaining water quality, mitigating climate change impacts, and providing a wide range of ecosystem services. Effective identification of mangrove species using remote-sensing images remains a challenge. The combinations of multi-source remote-sensing datasets (with different spectral/spatial resolution) are beneficial to the improvement of mangrove tree species discrimination. In this paper, various combinations of remote-sensing datasets including Sentinel-1 dual-polarimetric synthetic aperture radar (SAR), Sentinel-2 multispectral, and Gaofen-3 full-polarimetric SAR data were used to classify the mangrove communities in Xuan Thuy National Park, Vietnam. The mixture of mangrove communities consisting of small and shrub mangrove patches is generally difficult to separate using low/medium spatial resolution. To alleviate this problem, we propose to use label distribution learning (LDL) to provide the probabilistic mapping of tree species, including Sonneratia caseolaris (SC), Kandelia obovata (KO), Aegiceras corniculatum (AC), Rhizophora stylosa (RS), and Avicennia marina (AM). The experimental results show that the best classification performance was achieved by an integration of Sentinel-2 and Gaofen-3 datasets, demonstrating that full-polarimetric Gaofen-3 data is superior to the dual-polarimetric Sentinel-1 data for mapping mangrove tree species in the tropics.
Development of an Automated Visibility Analysis Framework for Pavement Markings Based on the Deep Learning Approach
Authors: Kang Kyubyung , Chen Donghui , Peng Cheng , Koo Dan , Kang Taewook , Kim Jonghoon
Published: November 2020

Pavement markings play a critical role in reducing crashes and improving safety on public roads. As road pavements age, maintenance work for safety purposes becomes critical. However, inspecting all pavement markings at the right time is very challenging due to the lack of available human resources. This study was conducted to develop an automated condition analysis framework for pavement markings using machine learning technology. The proposed framework consists of three modules: a data processing module, a pavement marking detection module, and a visibility analysis module. The framework was validated through a case study of pavement markings training data sets in the U.S. It was found that the detection model of the framework was very precise, which means most of the identified pavement markings were correctly classified. In addition, in the proposed framework, visibility was confirmed as an important factor of driver safety and maintenance, and visibility standards for pavement markings were defined.
Characterization of Intertidal Bar Morphodynamics Using a Bi-Annual LiDAR Dataset
Authors: Montreuil Anne-Lise , Moelans Robrecht , Houthuys Rik , Bogaert Patrick , Chen Margaret
Published: November 2020

Intertidal bars are common features on meso-and macro-tidal sandy beaches with low to moderate wave energy environments. Understanding their morphodynamics is, hence, crucial for enhancing our knowledge on beach processes which is beneficial for coastal management. However, most studies have been limited by assessing bar systems two-dimensionally and typically over the short-term. Morphology and dynamics of an intertidal bar system in a macro-tidal environment have been investigated using bi-annual LiDAR topographic surveys over a period of seven years and along 3.2 km at Groenendijk beach (Belgium). The detected bars demonstrate that a morphology of an intertidal bar is permanently on the beach. However, these individual features are dynamic and highly mobile over the course of half a year. The mean height and width of the bars were 1.1 and 82 m, respectively. The highest, steepest, and asymmetric features were found on the upper beach, while they were least developed in the lower intertidal zone. The bars were evenly distributed over the entire intertidal beach, but the largest concentration observed around the mean sea level indicated the occurrence at preferential locations. The most significant net change across the beach occurs between the mean sea level and mean-high-water neap which corroborates with the profile mobility pattern. The seasonal variability of the bar morphology is moderately related to the seasonally driven changes in storm and wave regime forcings. However, a distinct relationship may be inhibited by the complex combination of forcing-, relaxation time- and feedback-dominated response. This work conducted from bi-annual LiDAR surveys has provided an unprecedented insight into the complex spatial organization of intertidal bars as well as their variability in time from seasonal to annual scale.
A Reference Architecture for Interoperable Reservation Systems in Electric Vehicle Charging
Authors: Basmadjian Robert , Kirpes Benedikt , Mrkos Jan , Cuchý Marek
Published: November 2020

The charging infrastructure for electric vehicles faces the challenges of insufficient capacity and long charging duration. These challenges decrease the electric vehicle users’ satisfaction and lower the profits of infrastructure providers. Reservation systems can mitigate these issues. We introduce a reference architecture for interoperable reservation systems. The advantages of the proposed architecture are: it (1) considers the needs of the most relevant electric mobility stakeholders, (2) satisfies the interoperability requirements of existing technological heterogeneity, and (3) provides a classification of reservation types based on a morphological methodology. We instantiate the reference architecture and verify its interoperability and fulfillment of stakeholder requirements. Further, we demonstrate a proof-of-concept by instantiating and implementing an ad-hoc reservation approach. Our validation was based on simulations of real-world case studies for various reservation deployments in the Netherlands. We conclude that, in certain high demand situations, reservations can save significant time for electric vehicle trips. The findings indicate that a reservation system does not directly increase the utilization of the charging infrastructure.
Food Waste Behavior among Romanian Consumers: A Cluster Analysis
Authors: Pocol Cristina Bianca , Pinoteau Margaux , Amuza Antonio , Burlea-Schiopoiu Adriana , Glogovețan Alexandra-Ioana
Published: November 2020

The main objective of the current study was to conduct an investigation at the national level in order to identify types of consumers depending on their perception of food waste. To do this, an online survey was carried out on a sample of 2541 respondents. Using a K-means clustering model, three clusters of consumers were identified. Cluster 1, “Careless”, consumers who dispose of food and food waste carelessly, are people who come from rural areas or small cities and have a rather low level of education. They do not have much information on the subject of food waste and do not seem to consider it “a problem”. Cluster 2, “Precautious”, composed of responsible people who do not throw away food, are well-informed and consider that all actors in the agri-food chain have a share in the food waste problem. Those who belong to cluster 3, “Ignorant”, are mostly from urban areas and have a high level of education. They were not raised to prevent food waste and therefore lack such habits as adults. They have a fair awareness of this subject, but they do not consider themselves responsible for food waste. The results obtained indicate the need to continue the awareness and education campaigns initiated at governmental and civil society level.
Changes and Regional Differences in Urban Land Areas on Both Banks of the Strait of Malacca Based on Remote Sensing
Authors: Yang Fengshuo , Yang Xiaomei , Wang Zhihua , Liu Yueming , Liu Bin
Published: November 2020

Over the past 30 years, both banks of the Strait of Malacca have experienced rapid economic growth and urbanization. Because of the difficulties in obtaining multi-phase urban land information over a large tropical area, spatial-temporal evolution analysis of the urban land areas in this region has been restricted. In this study, we developed an object-oriented retro-updating method for the rapid extraction of multi-phase urban land information to conduct a comprehensive study of the urban land area changes and to investigate the regional differentiation of both banks of the Strait of Malacca from 1990 to 2018, using grid analysis, zoning statistics, and gradient-direction analysis. The results revealed that from 1990 to 2018, the urban land areas on the eastern and western banks of the Strait of Malacca had increased by 1.51 times and 1.76 times, respectively, and in 2018, the urban land areas on the eastern bank were approximately four times greater than those on the western bank. The urban land areas on the eastern bank exhibit clusters centered around large cities and small strips along roads. Furthermore, the development of the urban land on the western bank was slow. As of 2018, the low development intensity areas on the western bank accounted for 98.73% of the total area, with only Medan achieving a higher development intensity. The development speeds of both banks followed the same zonal law (i.e., decreasing with increasing distance from the coastline). In addition, the urban land areas on both banks were mainly distributed within 60 km of the coast. Over the past 30 years, in decreasing order of their expansion scales, the major ports on both banks of the strait are Port Kelang, the Port of Singapore, Malacca Port, Penang Port, Belawan Port, and Dumai Port. In addition to the Port of Singapore and Dumai Port, which are in leapfrog expansion mode, the other ports exhibit edge-expansion patterns. Finally, investigation of the factors influencing the intensity of the urban land development revealed that natural resource conditions, economic and industrial structures, port development, and government policies all have caused the development of the western bank to lag behind that of the eastern bank.
Academic Community in the Face of Emergency Situations: Sense of Responsible Togetherness and Sense of Belonging as Protective Factors against Academic Stress during COVID-19 Outbreak
Authors: Procentese Fortuna , Capone Vincenza , Caso Daniela , Donizzetti Anna , Gatti Flora
Published: November 2020

In the face of emergency situations, such as a global pandemic, individuals rely on their personal resources, but also on community dimensions, to deal with the unprecedented changes and risks and to safeguard their well-being. The present study specifically addresses the role of individual resources and community dimensions with reference to academic communities facing COVID-19-related lockdowns and the changes that these have implied. An online questionnaire was administered to 1124 Italian University students. It detected their sense of belonging and of responsible togetherness with reference to their academic community through community dimensions, their student self-efficacy as an individual resource, and their academic stress—potentially stemming from studying in the middle of a pandemic. A multiple mediation model was been run with structural equation modeling. The results show that both the community dimensions associate with higher student self-efficacy and the sense of responsible togetherness, while also associating with lower academic stress. Moreover, student self-efficacy, in turn, associates with lower academic stress and mediates the relationships between both community dimensions and students’ academic stress levels. From these findings, the protective role that community dimensions can exert on an individual’s life becomes apparent. Building on this, further strategies should be implemented to reinforce personal and community resources in order to strengthen individuals against potentially stressful circumstances.
Bayesian Model Averaging: A Unique Model Enhancing Forecasting Accuracy for Daily Streamflow Based on Different Antecedent Time Series
Authors: Kim Sungwon , Alizamir Meysam , Kim Nam Won , Kisi Ozgur
Published: November 2020

Streamflow forecasting is a vital task for hydrology and water resources engineering, and the different artificial intelligence (AI) approaches have been employed for this purposes until now. Additionally, the forecasting accuracy and uncertainty estimation are the meaningful assignments that need to be recognized. The addressed research investigates the potential of novel ensemble approach, Bayesian model averaging (BMA), in streamflow forecasting using daily time series data from two stations (i.e., Hongcheon and Jucheon), South Korea. Six categories (i.e., M1–M6) of input combination using different antecedent times were employed for streamflow forecasting. The outcomes of BMA model were compared with those of multivariate adaptive regression spline (MARS), M5 model tree (M5Tree), and Kernel extreme learning machines (KELM) models considering four assessment indexes, root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), correlation coefficient (R), and mean absolute error (MAE). The results revealed the superior accuracy of BMA model over three machine learning models in daily streamflow forecasting. Considering RMSE values among the best models during testing phase, the best BMA model (i.e., BMA2) enhanced the forecasting accuracy of MARS1, M5Tree4, and KELM3 models by 5.2%, 5.8%, and 3.4% in Hongcheon station. Additionally, the best BMA model (i.e., BMA1) improved the forecasting accuracy of MARS1, M5Tree1, and KELM1 models by 6.7%, 9.5%, and 3.7% in Jucheon station. In addition, the best BMA models in both stations allowed the uncertainty estimation, and produced higher uncertainty of peak flows compared to that of low flows. As one of the most robust and effective tools, therefore, the BMA model can be successfully employed for streamflow forecasting with different antecedent times.
Nuclear Hazard and Asset Prices: Implications of Nuclear Disasters in the Cross-Sectional Behavior of Stock Returns
Authors: Alonso-Conde Ana Belén , Rojo-Suárez Javier
Published: November 2020

Using stock return data for the Japanese equity market, for the period from July 1983 to June 2018, we analyze the effect of major nuclear disasters worldwide on Japanese discount rates. For that purpose, we compare the performance of the capital asset pricing model (CAPM) conditional on the event of nuclear disasters with that of the classic CAPM and the Fama–French three- and five-factor models. In order to control for nuclear disasters, we use an instrument that allows us to parameterize the linear stochastic discount factor of the conditional CAPM and transform the classic CAPM into a three-factor model. In this regard, the use of nuclear disasters as an explanatory variable for the cross-sectional behavior of stock returns is a novel contribution of this research. Our results suggest that nuclear disasters account for a large fraction of the variation of stock returns, allowing the CAPM to perform similarly to the Fama–French three- and five-factor models. Furthermore, our results show that, in general, nuclear disasters are positively related to the expected returns of a large number of assets under study. Our results have important implications for the task of estimating the cost of equity and constitute a step forward in understanding the relationship between equity risk premiums and nuclear disasters.
An Introduction to Aboriginal Fishing Cultures and Legacies in Seafood Sustainability
Authors: Shamsi Shokoofeh , Williams Michelle , Mansourian Yazdan
Published: November 2020

The purpose of this paper was to explore the rich legacy of Aboriginal fishing cultures through historical and contemporary records, in order to inductively identify cultural and social elements which may enhance the aquatic resource sustainability knowledge and ethos in Australia. A thorough, comprehensive and analytical literature review was conducted. The literature review explored the importance of Aboriginal Peoples’ (AP) fishing cultures, and identified examples of fishing practices, ideology and sustainable philosophy, which may be beneficial in sustainably managing dwindling seafood resources. The overriding theme of Aboriginal Peoples’ fishing cultures is the taking of enough fish to ease personal/community hunger, and restricted fishing based on seasons and/or stock abundance. This practice protects fish during vulnerable or important stages, such as spawning, allowing fish stocks to regenerate, and allowing fish to be caught when they are healthy and most nutritious. It is considered that the Aboriginal Peoples of Australia can contribute significant knowledge to the management of dwindling aquatic resources. Access to sacred sites and favourite fishing grounds would benefit communities, would increase the contact between Aboriginal and non-Aboriginal Peoples, and may assist in an interchange of sustainability information and philosophy. The world’s aquatic resources are dangerously depleted. Without a significant shift in focus, this will continue. Rather than relegate the fishing cultures of Aboriginal Peoples to ‘histories past’, we can learn valuable lessons to conserve aquatic resources, and to better understand the interconnectedness with the environment inherent in their cultures. Fishing is used as a generic term for both freshwater and marine fishing in this article.
Variability of the Motor Behavior during Continued Practice of the Same Motor Game: A Preliminary Study
Authors: Gonzalez-Artetxe Asier , Pino-Ortega José , Rico-González Markel , Los Arcos Asier
Published: November 2020

Motor behavior assessment during games could help physical education teachers and team coaches to design effective and efficient motor interventions. This study aimed to assess the variability of the physical and behavioral responses during continued practice of the game tail tag with a ball. Sixteen Spanish youth soccer players from an under-14 team played tail tag, with a ball, during four sessions (5 repetitions of 1 min per session). Physical (i.e., Total Distance (TD) and PlayerLoad (PL)) and behavioral (i.e., Surface Area (SA) and change in the Geometrical Centre position (cGCp)) dimensions were assessed with a local positioning system. The mean of the five series of each session was considered for further statistical analysis. The main finding was that the external load decreased (d = small − large) and the use of space varied during the continued practice of tail tag. Initially, SA increased substantially (d = large) and cGCp decreased slightly (d = small), and then both variables tended to stabilize. This suggests that after several repetitions of the same motor game, physical education teachers and team sports coaches should use this again later, modifying this or proposing new motor games where players respond to these activities that consist in greater uncertainty than to well-known motor games.