bluuebunny/crossref_metadata_2025 huggingface dataset:
Study and Characterization of the Dielectric Behavior of Low Linear Density Polyethylene Composites Mixed with Ground Tire Rubber Particles
Authors: Marín-Genescà Marc , García-Amorós Jordi , Mujal-Rosas Ramon , Massagués Lluís , Colom Xavier
Published: May 2020

The waste rubber vulcanizate, on account of its stable, cross-linked and three-dimensional structural arrangement, is difficult to biodegrade. Thus, the ever-increasing bulk of worn-out tires is a serious environmental issue and its safe disposal is still a challenging task reported widely by the scientific community. The rubber materials, once they end their useful life, may present difficulties to be reused or recycled. At present, only one tire recycling method is used, which involves grinding and separating steel and fibers from vulcanized rubber, and then using rubber for industrial applications, such as flooring, insulation, footwear. In this paper, a new compound material is presented from a base of reused tire powder (Ground Tire Rubber: GTR) as a mixer and linear low-density polyethylene (LLDPE) as a matrix. The reused tire powder, resulting from grinding industrial processes, is separated by sieving into just one category of particle size (<200 μm) and mixed with the LLDPE in different amounts (0%, 5%, 10%, 20%, 40%, 50% and 70% GTR). Due to the good electrical properties of the LLDPE, this study’s focus is settled on the electrical behavior of the obtained composites. The test of the dielectric behavior is carried out by means of DEA test (Dynamic Electric Analysis), undertaken at a range of temperatures varying from 30 to 120 °C, and with a range of frequencies from 1 to 102, to 3·106 Hz, from which permittivity, conductivity, dielectric constant and electric modulus have been obtained. From these experimental results and their analysis, it can be drawn that the additions of different quantities of GTR to LLDPE could be used as industrial applications, such as universal electrical cable joint, filler for electrical applications or cable tray systems and cable ladder system.
Stable Non-Covalent Co(Salphen)-Based Polymeric Catalyst for Highly Efficient and Selective Oxidation of 2,3,6-Trimethylphenol
Authors: Zhang Weijie , Hu Lingling , Zhang He , Pan Chunyue , Tang Juntao
Published: May 2020

Developing highly efficient catalyst systems for phenol–quinone transformation is of great significance in the chemical/biological industries. Herein, we reported a novel heterogenous catalytic system based on Co(Salphen) supramolecular polymers (CSP), which delivered an excellent catalytic performance in the oxidation of 2,3,6-trimethylphenol (TMP) under mild conditions. The CSP were constructed through a simple self-assembled process between BiCo(Salphen) complex and 4,4-dipyridine. By applying BiCo-BiPy1:1 CSP as the catalyst, 2,3,5-trimethyl-1,4-benzoquinone (TMBQ) could be obtained with an excellent conversion (>99%) and selectivity over 99% under mild reaction conditions (30 °C, 0.1 MPa). In addition, it can be recycled at least five times without substantial decline in catalytic activities (conversion and selectivity), suggesting its excellent stability and recyclability. This work may provide guidance on designing and building valuable catalysts for environmentally friendly and cost-effective oxidation reactions.
Poly (vinyl alcohol)/β-Cyclodextrin Composite Fiber with Good Flame Retardant and Super-Smoke Suppression Properties
Authors: Xing Cheng-Yuan , Zeng Shi-Lin , Qi Shi-Kai , Jiang Meng-Jin , Xu Long , Chen Li , Zhang Sheng , Li Bang-Jing
Published: May 2020

Fibers with good flame retardant (FR) and smoke suppression performances are highly desirable for the purpose of eliminating fire hazard. This study developed a novel FR fiber by wet-spinning poly (vinyl alcohol)/β-cyclodextrin (PVA/βCD) composite fiber and crosslinking it with hexamethylene diisocyanate (HDI). βCDs showed good compatibility with PVA matrix, and the resulting PVA/CD/HDI fibers showed mechanical strength at the same level as natural cotton fiber. The PVA/CD/HDI fibers also showed excellent flame retardance (the LOI value of PVA/CD/HDI could reach 41.7%, and their peak of heat release (PHRR) could be reduced by up to 77.7% by neat PVA), and super-smoke suppression (the value of total smoke production (TSP) was only 28.6% compared to PVA). These dramatic reductions of fire hazard were ascribed to the char formation of βCD and crosslinking structure of PVA/CD/HDI, which formed a compact char layer during combustion, thus preventing heat transmission and smoke release.
Polypropylene/Basalt Fabric Laminates: Flexural Properties and Impact Damage Behavior
Authors: Russo Pietro , Papa Ilaria , Pagliarulo Vito , Lopresto Valentina
Published: May 2020

Recently, the growing interests into the environmental matter are driving the research interest to the development of new eco-sustainable composite materials toward the replacement of synthetic reinforcing fibers with natural ones and exploiting the intrinsic recyclability of thermoplastic resins even for uses in which thermosetting matrices are well consolidated (e.g., naval and aeronautical fields). In this work, polypropylene/basalt fabric composite samples were prepared by film stacking and compression molding procedures. They have been studied in terms of flexural and low-velocity impact behavior. The influence related to the matrix modification with a pre-optimized amount of maleic anhydride grafted PP as coupling agent was studied. The mechanical performances of the composite systems were compared with those of laminates consisting of the pure matrix and obtained by hot-pressing of PP pellets and PP films used in the stacking procedure. Results, on one side, demonstrated a slight reduction of both static and dynamic parameters at the break for specimens from superimposed films to ones prepared from PP pellets. Moreover, an outstanding improvement of mechanical performances was shown in the presence of basalt layers, especially for compatibilized samples.
Green Pathway in Utilizing CO2 via Cycloaddition Reaction with Epoxide—A Mini Review
Authors: Kiatkittipong Kunlanan , Mohamad Shukri Muhammad Amirul Amin , Kiatkittipong Worapon , Lim Jun Wei , Show Pau Loke , Lam Man Kee , Assabumrungrat Suttichai
Published: May 2020

Carbon dioxide (CO2) has been anticipated as an ideal carbon building block for organic synthesis due to the noble properties of CO2, which are abundant renewable carbon feedstock, non-toxic nature, and contributing to a more sustainable use of resources. Several green and proficient routes have been established for chemical CO2 fixation. Among the prominent routes, this review epitomizes the reactions involving cycloaddition of epoxides with CO2 in producing cyclic carbonate. Cyclic carbonate has been widely used as a polar aprotic solvent, as an electrolyte in Li-ion batteries, and as precursors for various forms of chemical synthesis such as polycarbonates and polyurethanes. This review provides an overview in terms of the reaction mechanistic pathway and recent advances in the development of several classes of catalysts, including homogeneous organocatalysts (e.g., organic salt, ionic liquid, deep eutectic solvents), organometallic (e.g., mono-, bi-, and tri-metal salen complexes and non-salen complexes) and heterogeneous supported catalysts, and metal organic framework (MOF). Selection of effective catalysts for various epoxide substrates is very important in determining the cycloaddition operating condition. Under their catalytic systems, all classes of these catalysts, with regard to recent developments, can exhibit CO2 cycloaddition of terminal epoxide substrates at ambient temperatures and low CO2 pressure. Although highly desired conversion can be achieved for internal epoxide substrates, higher temperature and pressure are normally required. This includes fatty acid-derived terminal epoxides for oleochemical carbonate production. The production of fully renewable resources by employment of bio-based epoxy with biorefinery concept and potential enhancement of cycloaddition reactions are pointed out as well.
Review of Evidence Available on Hesperidin-Rich Products as Potential Tools against COVID-19 and Hydrodynamic Cavitation-Based Extraction as a Method of Increasing Their Production
Authors: Meneguzzo Francesco , Ciriminna Rosaria , Zabini Federica , Pagliaro Mario
Published: May 2020

Based on recent computational and experimental studies, hesperidin, a bioactive flavonoid abundant in citrus peel, stands out for its high binding affinity to the main cellular receptors of SARS-CoV-2, outperforming drugs already recommended for clinical trials. Thus, it is very promising for prophylaxis and treatment of COVID-19, along with other coexistent flavonoids such as naringin, which could help restraining the proinflammatory overreaction of the immune system. Controlled hydrodynamic cavitation processes showed the highest speed, effectiveness and efficiency in the integral and green aqueous extraction of flavonoids, essential oils and pectin from citrus peel waste. After freeze-drying, the extracted pectin showed high quality and excellent antioxidant and antibacterial activities, attributed to flavonoids and essential oils adsorbed and concentrated on its surface. This study reviews the recent evidence about hesperidin as a promising molecule, and proposes a feasible and affordable process based on hydrodynamic cavitation for the integral aqueous extraction of citrus peel waste resulting in hesperidin-rich products, either aqueous extracts or pectin tablets. The uptake of this process on a relevant scale is urged, in order to achieve large-scale production and distribution of hesperidin-rich products. Meanwhile, experimental and clinical studies could determine the effective doses either for therapeutic and preventive purposes.
Special Issue: Smart Flow Control in Micro Scale
Authors: Qian Jin-yuan , Zhang Junhui , Wu Zan , Sunden Bengt
Published: May 2020

Smart control processes have been proposed for many years, while for smart flow control—especially when “smart flow control” comes at the microscale—it turns out that many new innovations and enabling technologies are possible [...]
Terpene Based Elastomers: Synthesis, Properties, and Applications
Authors: Sahu Pranabesh , Bhowmick Anil K , Kali Gergely
Published: May 2020

The limited source of fossil-fuel and the predominance of petroleum-based chemistry in the manufacturing of commodity polymers has generated tremendous interest in replacing the fossil source-based polymers with renewable counterparts. The field of sustainable elastomers has grown in the past three decades, from a few examples to a plethora of reports in modern polymer science and technology. Applications of elastomers are huge and vital for everyday living. The present review aims to portray a birds-eye view of various sustainable elastomers obtained from the wide family of acyclic terpenes (renewable feedstocks from different plant oils) via various polymerization techniques and their properties, as well as plausible developments in the future applications of sustainable polymers. Not only the homopolymers, but also their copolymers with both green and commercial fossil based comonomers, are reviewed.
Syncretism, Harmonization, and Mutual Appropriation between Buddhism and Confucianism in Pre-Joseon Korea
Authors: Vermeersch Sem
Published: May 2020

Following the introduction of Buddhism to China, various strategies of accommodation with Chinese culture were developed, all amounting to some form of syncretism with Chinese religions, mainly Confucianism. Buddhism in pre-modern Korea displayed similar forms of interaction with Confucianism. This article aims to critique the notion that such interactions were merely forms of “harmonization”, finding common ground between the traditions. If one religion borrows from another or adopts the message of another religion, it will be affected to some degree, which is why the concept of syncretism is a better tool of analysis. This article concludes that there was a strong official support in Goryeo Korea towards the genuine convergence of Confucianism and Buddhism. Since Buddhism, as a result, took on many of the tasks carried out by Confucianism in China, the reaction against Buddhism by a reinvigorated Confucianism from the late fourteenth century onward was much stronger than in China.
Picasso for Preaching: The Demand and Possibility of a Cubist Homiletic
Authors: Yang Sunggu
Published: May 2020

The purpose of this article is to propose a cubist homiletic based on the Picasso-originated art movement known as cubism. To that end, I explore the twofold question: What is cubist preaching, and why do we need it today? It is a critical inquiry into a theology and methodology of cubist preaching and its contextual rationale. In particular, I adopt cubism’s artistic-philosophical routine of transcendental deconstruction and multi-perspectival reconstruction as the key hermeneutical and literary methodology for cubist preaching. This cubist way of preaching ultimately aims for the listener to encounter the Sacred in what I call an ubi-ductive way—a neologism made by conjoining the two terms, ubiquitous and -ductive, beyond what is possible through conventional inductive and deductive preaching.
Towards an Economic Cyber Loss Index for Parametric Cover Based on IT Security Indicator: A Preliminary Analysis
Authors: Dal Moro Eric
Published: May 2020

As cyber events have virtually no geographical limitations and can result in economic losses on a global scale, the assessment of return periods for such economic losses is currently debated among experts. The potential accumulation of consequential insurance losses due to intrusions or viruses is one of the major reasons why the (re-)insurance industry has limited risk appetite for cyber related risks. In order to increase the risk appetite for cyber risk and based on a first batch of data provided by Symantec, the goal of this article is to: Check if IT activity, i.e., the number of virus or intrusions being blocked by Norton on end-user computers could be used as an index for parametric covers that reinsurance companies could propose to their cedants; Look into the correlations of this IT activity across different regions, thereby confirming the absence of geographical limitations for cyber risk, and hence confirming the systemic nature of this risk. This first study on the Symantec dataset shows that a cyber index based on IT activity could be a useful tool to design parametric reinsurance product.
Assessing Soil Erosion Hazards Using Land-Use Change and Landslide Frequency Ratio Method: A Case Study of Sabaragamuwa Province, Sri Lanka
Authors: Senanayake Sumudu , Pradhan Biswajeet , Huete Alfredo , Brennan Jane
Published: May 2020

This study aims to identify the vulnerable landscape areas using landslide frequency ratio and land-use change associated soil erosion hazard by employing geo-informatics techniques and the revised universal soil loss equation (RUSLE) model. Required datasets were collected from multiple sources, such as multi-temporal Landsat images, soil data, rainfall data, land-use land-cover (LULC) maps, topographic maps, and details of the past landslide incidents. Landsat satellite images from 2000, 2010, and 2019 were used to assess the land-use change. Geospatial input data on rainfall, soil type, terrain characteristics, and land cover were employed for soil erosion hazard classification and mapping. Landscape vulnerability was examined on the basis of land-use change, erosion hazard class, and landslide frequency ratio. Then the erodible hazard areas were identified and prioritized at the scale of river distribution zones. The image analysis of Sabaragamuwa Province in Sri Lanka from 2000 to 2019 indicates a significant increase in cropping areas (17.96%) and urban areas (3.07%), whereas less dense forest and dense forest coverage are significantly reduced (14.18% and 6.46%, respectively). The average annual soil erosion rate increased from 14.56 to 15.53 t/ha/year from year 2000 to 2019. The highest landslide frequency ratios are found in the less dense forest area and cropping area, and were identified as more prone to future landslides. The river distribution zones Athtanagalu Oya (A-2), Kalani River-south (A-3), and Kalani River- north (A-9), were identified as immediate priority areas for soil conservation.
Ice Production in Ross Ice Shelf Polynyas during 2017–2018 from Sentinel–1 SAR Images
Authors: Dai Liyun , Xie Hongjie , Ackley Stephen F. , Mestas-Nuñez Alberto M.
Published: May 2020

High sea ice production (SIP) generates high-salinity water, thus, influencing the global thermohaline circulation. Estimation from passive microwave data and heat flux models have indicated that the Ross Ice Shelf polynya (RISP) may be the highest SIP region in the Southern Oceans. However, the coarse spatial resolution of passive microwave data limited the accuracy of these estimates. The Sentinel-1 Synthetic Aperture Radar dataset with high spatial and temporal resolution provides an unprecedented opportunity to more accurately distinguish both polynya area/extent and occurrence. In this study, the SIPs of RISP and McMurdo Sound polynya (MSP) from 1 March–30 November 2017 and 2018 are calculated based on Sentinel-1 SAR data (for area/extent) and AMSR2 data (for ice thickness). The results show that the wind-driven polynyas in these two years occurred from the middle of March to the middle of November, and the occurrence frequency in 2017 was 90, less than 114 in 2018. However, the annual mean cumulative SIP area and volume in 2017 were similar to (or slightly larger than) those in 2018. The average annual cumulative polynya area and ice volume of these two years were 1,040,213 km2 and 184 km3 for the RSIP, and 90,505 km2 and 16 km3 for the MSP, respectively. This annual cumulative SIP (volume) is only 1/3–2/3 of those obtained using the previous methods, implying that ice production in the Ross Sea might have been significantly overestimated in the past and deserves further investigations.
Regional Forest Mapping over Mountainous Areas in Northeast China Using Newly Identified Critical Temporal Features of Sentinel-1 Backscattering
Authors: Yu Haoyang , Ni Wenjian , Zhang Zhongjun , Sun Guoqing , Zhang Zhiyu
Published: May 2020

Sentinel-1 provides an extraordinary opportunity to explore the temporal behavior of backscattering of C-band synthetic aperture radar (SAR) due to its unique capability of successive observations every 12 days. This study reported new findings on the critical temporal features of Sentinel-1 backscattering over mountainous forested areas in northeast China and their application in regional forest mapping. Two interesting phenomena were discovered through the analysis of 450 scenes of images acquired by Sentinel-1A or Sentinel-1B over an area of 318,898.62 km2. The first phenomenon was that the dates of the largest drops of backscattering coefficients over forest and non-forest plots were different during the transition from autumn to winter. The largest drop of non-forest plots occurred around the date of the minimum temperature decreasing below 0 °C, while that of forest plots occurred around the date of the maximum temperature decreasing below 0 °C. The second phenomenon was that at the dates where these two drops occurred, the magnitude of the drop was negatively correlated with the forest canopy coverage for the first date and positively correlated for the second date. Based on these two phenomena, two methods for the forest mapping, referred to as the direct method and the indirect method, were proposed using only three dates of Sentinel-1 images, i.e., Date1: before the minimum temperature decreased below 0 °C, Date2: after the minimum temperature decreased below 0 °C but before the maximum temperature decreased below 0 °C, and Date3: after the maximum temperature decreased below 0 °C. The results showed that the overall accuracy of the forest map produced by the direct method was 93.60%, while that by the indirect method was 93.80%. Their accuracies were comparable with those of forest maps derived from publicly released land cover maps, which was approximately 94.42% for the best one. This study proposed a new way to do large-scale forest mapping in annually frozen regions using as few Sentinel-1 SAR images as possible.
Integration of Remotely Sensed Soil Sealing Data in Landslide Susceptibility Mapping
Authors: Luti Tania , Segoni Samuele , Catani Filippo , Munafò Michele , Casagli Nicola
Published: May 2020

Soil sealing is the destruction or covering of natural soils by totally or partially impermeable artificial material. ISPRA (Italian Institute for Environmental Protection Research) uses different remote sensing techniques to monitor this process and updates yearly a national-scale soil sealing map of Italy. In this work, for the first time, we tried to combine soil sealing indicators as additional parameters within a landslide susceptibility assessment. Four new parameters were derived from the raw soil sealing map: Soil sealing aggregation (percentage of sealed soil within each mapping unit), soil sealing (categorical variable expressing if a mapping unit is mainly natural or sealed), urbanization (categorical variable subdividing each unit into natural, semi-urbanized, or urbanized), and roads (expressing the road network disturbance). These parameters were integrated with a set of well-established explanatory variables in a random forest landslide susceptibility model and different configurations were tested: Without the proposed soil-sealing-derived variables, with all of them contemporarily, and with each of them separately. Results were compared in terms of AUC ((area under receiver operating characteristics curve, expressing the overall effectiveness of each configuration) and out-of-bag-error (estimating the relative importance of each variable). We found that the parameter “soil sealing aggregation” significantly enhanced the model performances. The results highlight the potential relevance of using soil sealing maps on landslide hazard assessment procedures.
Analysis of the Occurrence Frequency of Seedable Clouds on the Korean Peninsula for Precipitation Enhancement Experiments
Authors: Kim Bu-Yo , Cha Joo Wan , Ko A-Reum , Jung Woonseon , Ha Jong-Chul
Published: May 2020

Our study analyzed the occurrence frequency and distribution of seedable clouds around the Korean Peninsula in order to better secure water resources. Cloud products from the Communication, Ocean, and Meteorological Satellite (COMS), including cloud fraction, cloud top height, cloud top temperature, cloud phase, cloud top pressure, cloud optical thickness, and rainfall intensity, were used. Daytime hourly data between 0900 and 1800 local standard time (LST) observed from December 2016 to November 2019 was used. Seedable clouds occurring within this period were evaluated based on seasonal cloud phases, occurrence frequency, and cloud characteristics according to land, sea, and cloud type. These clouds exhibited varying average occurrence frequencies in different seasons. Sc (stratocumulus) clouds exhibited the highest occurrence frequency for all seasons, with an average of 63%, followed by Cu (cumulus) at 15%, As (altostratus) at 13%, and Ac (altocumulus) at 6%. We determined that low-level clouds primarily occurred around the Korean Peninsula, and the occurrence frequency of stratiform clouds was highest for water phase seedable clouds, while the occurrence frequency of cumuliform clouds was highest for ice phase seedable clouds. We believe that precipitation enhancement experiments could be suitable for western and eastern seas around the Korean Peninsula as well as for mountainous regions on land.
HTD-Net: A Deep Convolutional Neural Network for Target Detection in Hyperspectral Imagery
Authors: Zhang Gaigai , Zhao Shizhi , Li Wei , Du Qian , Ran Qiong , Tao Ran
Published: May 2020

In recent years, deep learning has dramatically improved the cognitive ability of the network by extracting depth features, and has been successfully applied in the field of feature extraction and classification of hyperspectral images. However, it is facing great difficulties for target detection due to extremely limited available labeled samples that are insufficient to train deep networks. In this paper, a novel target detection framework for deep learning is proposed, denoted as HTD-Net. To overcome the few-training-sample issue, the proposed framework utilizes an improved autoencoder (AE) to generate target signatures, and then finds background samples which differ significantly from target samples based on a linear prediction (LP) strategy. Then, the obtained target and background samples are used to enlarge the training set by generating pixel-pairs, which is viewed as the input of a pre-designed network architecture to learn discriminative similarity. During testing, pixel-pairs of a pixel to be labeled are constructed with both available target samples and background samples. Spectral difference between these pixel-pairs is classified by the well-trained network with results of similarity measurement. The outputs from a two-branch averaged similarity scores are combined to generate the final detection. Experimental results with several real hyperspectral data demonstrate the superiority of the proposed algorithm compared to some traditional target detectors.
Applications of UAV Thermal Imagery in Precision Agriculture: State of the Art and Future Research Outlook
Authors: Messina Gaetano , Modica Giuseppe
Published: May 2020

Low-altitude remote sensing (RS) using unmanned aerial vehicles (UAVs) is a powerful tool in precision agriculture (PA). In that context, thermal RS has many potential uses. The surface temperature of plants changes rapidly under stress conditions, which makes thermal RS a useful tool for real-time detection of plant stress conditions. Current applications of UAV thermal RS include monitoring plant water stress, detecting plant diseases, assessing crop yield estimation, and plant phenotyping. However, the correct use and interpretation of thermal data are based on basic knowledge of the nature of thermal radiation. Therefore, aspects that are related to calibration and ground data collection, in which the use of reference panels is highly recommended, as well as data processing, must be carefully considered. This paper aims to review the state of the art of UAV thermal RS in agriculture, outlining an overview of the latest applications and providing a future research outlook.
A Global Analysis of Anthropogenic Development of Marine Turtle Nesting Beaches
Authors: Biddiscombe Sarah J. , Smith Elliott A. , Hawkes Lucy A.
Published: May 2020

The Intergovernmental Panel on Climate Change predicts that sea levels will rise by up to 0.82 m in the next 100 years. In natural systems, coastlines would migrate landwards, but because most of the world’s human population occupies the coast, anthropogenic structures (such as sea walls or buildings) have been constructed to defend the shore and prevent loss of property. This can result in a net reduction in beach area, a phenomenon known as “coastal squeeze”, which will reduce beach availability for species such as marine turtles. As of yet, no global assessment of potential future coastal squeeze risk at marine turtle nesting beaches has been conducted. We used Google Earth satellite imagery to enumerate the proportion of beaches over the global nesting range of marine turtles that are backed by hard anthropogenic coastal development (HACD). Mediterranean and North American nesting beaches had the most HACD, while the Australian and African beaches had the least. Loggerhead and Kemp’s ridley turtle nesting beaches had the most HACD, and flatback and green turtles the least. Future management approaches should prioritise the conservation of beaches with low HACD to mitigate future coastal squeeze.
Estimating Daily Inundation Probability Using Remote Sensing, Riverine Flood, and Storm Surge Models: A Case of Hurricane Harvey
Authors: Liang Jiayong , Liu Desheng
Published: May 2020

Heavy precipitation and storm surges often co-occur and compound together to form sudden and severe flooding events. However, we lack comprehensive observational tools with high temporal and spatial resolution to capture these fast-evolving hazards. Remotely sensed images provide extensive spatial coverage, but they may be limited by adverse weather conditions or platform revisiting schedule. River gauges could provide frequent water height measurement but they are sparsely distributed. Riverine flood and storm surge models, depending on input data quality and calibration process, have various uncertainties. These lead to inevitable temporal and spatial gaps in monitoring inundation dynamics. To fill in the observation gaps, this paper proposes a probabilistic method to estimate daily inundation probability by combining the information from multiple sources, including satellite remote sensing, riverine flood depth, storm surge height, and land cover. Each data source is regarded as a spatial evidence layer, and the weight of evidence is calculated by assessing the association between the evidence presence and inundation occurrence. Within a Bayesian model, the fusion results are daily inundation probability whenever at least one data source is available. The proposed method is applied to estimate daily inundation in Harris, Texas, impacted by Hurricane Harvey. The results agree with the reference water extent, high water mark, and extracted tweet locations. This method could help to further understand flooding as an evolving time-space process and support response and mitigation decisions.
Comparison of Statistical Modelling Approaches for Estimating Tropical Forest Aboveground Biomass Stock and Reporting Their Changes in Low-Intensity Logging Areas Using Multi-Temporal LiDAR Data
Authors: Rex Franciel Eduardo , Silva Carlos Alberto , Dalla Corte Ana Paula , Klauberg Carine , Mohan Midhun , Cardil Adrián , Silva Vanessa Sousa da , Almeida Danilo Roberti Alves de , Garcia Mariano , Broadbent Eben North , Valbuena Ruben , Stoddart Jaz , Merrick Trina , Hudak Andrew Thomas
Published: May 2020

Accurately quantifying forest aboveground biomass (AGB) is one of the most significant challenges in remote sensing, and is critical for understanding global carbon sequestration. Here, we evaluate the effectiveness of airborne LiDAR (Light Detection and Ranging) for monitoring AGB stocks and change (ΔAGB) in a selectively logged tropical forest in eastern Amazonia. Specifically, we compare results from a suite of different modelling methods with extensive field data. The calibration AGB values were derived from 85 square field plots sized 50 × 50 m field plots established in 2014 and which were estimated using airborne LiDAR data acquired in 2012, 2014, and 2017. LiDAR-derived metrics were selected based upon Principal Component Analysis (PCA) and used to estimate AGB stock and change. The statistical approaches were: ordinary least squares regression (OLS), and nine machine learning approaches: random forest (RF), several variations of k-nearest neighbour (k-NN), support vector machine (SVM), and artificial neural networks (ANN). Leave-one-out cross-validation (LOOCV) was used to compare performance based upon root mean square error (RMSE) and mean difference (MD). The results show that OLS had the best performance with an RMSE of 46.94 Mg/ha (19.7%) and R² = 0.70. RF, SVM, and ANN were adequate, and all approaches showed RMSE ≤54.48 Mg/ha (22.89%). Models derived from k-NN variations all showed RMSE ≥64.61 Mg/ha (27.09%). The OLS model was thus selected to map AGB across the time-series. The mean (±sd—standard deviation) predicted AGB stock at the landscape level was 229.10 (±232.13) Mg/ha in 2012, 258.18 (±106.53) in 2014, and 240.34 (sd ± 177.00) Mg/ha in 2017, showing the effect of forest growth in the first period and logging in the second period. In most cases, unlogged areas showed higher AGB stocks than logged areas. Our methods showed an increase in AGB in unlogged areas and detected small changes from reduced-impact logging (RIL) activities occurring after 2012. We also detected that the AGB increase in areas logged before 2012 was higher than in unlogged areas. Based on our findings, we expect our study could serve as a basis for programs such as REDD+ and assist in detecting and understanding AGB changes caused by selective logging activities in tropical forests.
Identifying Post-Fire Recovery Trajectories and Driving Factors Using Landsat Time Series in Fire-Prone Mediterranean Pine Forests
Authors: Viana-Soto Alba , Aguado Inmaculada , Salas Javier , García Mariano
Published: May 2020

Wildfires constitute the most important natural disturbance of Mediterranean forests, driving vegetation dynamics. Although Mediterranean species have developed ecological post-fire recovery strategies, the impacts of climate change and changes in fire regimes may endanger their resilience capacity. This study aims at assessing post-fire recovery dynamics at different stages in two large fires that occurred in Mediterranean pine forests (Spain) using temporal segmentation of the Landsat time series (1994–2018). Landsat-based detection of Trends in Disturbance and Recovery (LandTrendr) was used to derive trajectory metrics from Tasseled Cap Wetness (TCW), sensitive to canopy moisture and structure, and Tasseled Cap Angle (TCA), related to vegetation cover gradients. Different groups of post-fire trajectories were identified through K-means clustering of the Recovery Ratios (RR) from fitted trajectories: continuous recovery, continuous recovery with slope changes, continuous recovery stabilized and non-continuous recovery. The influence of pre-fire conditions, fire severity, topographic variables and post-fire climate on recovery rates for each recovery category at successional stages was analyzed through Geographically Weighted Regression (GWR). The modeling results indicated that pine forest recovery rates were highly sensitive to post-fire climate in the mid and long-term and to fire severity in the short-term, but less influenced by topographic conditions (adjusted R-squared ranged from 0.58 to 0.88 and from 0.54 to 0.93 for TCA and TCW, respectively). Recovery estimation was assessed through orthophotos, showing a high accuracy (Dice Coefficient ranged from 0.81 to 0.97 and from 0.74 to 0.96 for TCA and TCW, respectively). This study provides new insights into the post-fire recovery dynamics at successional stages and driving factors. The proposed method could be an approach to model the recovery for the Mediterranean areas and help managers in determining which areas may not be able to recover naturally.
Variable Urbanization Warming Effects across Metropolitans of China and Relevant Driving Factors
Authors: Zhang Qiang , Wu Zixuan , Yu Huiqian , Zhu Xiudi , Shen Zexi
Published: May 2020

Urbanization is mainly characterized by the expansion of impervious surface (IS) and hence modifies hydrothermal properties of the urbanized areas. This process results in rising land surface temperature (LST) of the urbanized regions, i.e., urban heat island (UHI). Previous studies mainly focused on relations between LST and IS over individual city. However, because of the spatial heterogeneity of UHI from individual cities to urban agglomerations and the influence of relevant differences in climate background across urban agglomerations, the spatial-temporal scale independence of the IS-LST relationship still needs further investigation. In this case, based on Landsat-8 Operational Land Imager and Thermal Infrared Sensor (Landsat 8 OLI/TIRS) remote sensing image and multi-source remote sensing data, we extracted IS using VrNIR-BI (Visible red and NIR-based built-up Index) and calculated IS density across three major urban agglomerations across eastern China, i.e., the Beijing-Tianjin-Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD) to investigate the IS-LST relations on different spatial and temporal scales and clarify the driving factors of LST. We find varying warming effects of IS on LST in diurnal and seasonal sense at different time scales. Specifically, the IS has stronger impacts on increase of LST during daytime than during nighttime and stronger impacts on increase of LST during summer than during winter. On different spatial scales, more significant enhancing effects of IS on LST can be observed across individual city than urban agglomerations. The Pearson correlation coefficient (r) between IS and LST at the individual urbanized region can be as high as 0.94, indicating that IS can well reflect LST changes within individual urbanized region. However, relationships between IS and LST indicate nonlinear effects of IS on LST. Because of differences in spatial scales, latitudes, and local climates, we depicted piecewise linear relations between IS and LST across BTH when the IS density was above 10% to 17%. Meanwhile, linear relations still stand between IS density and LST across YRD and PRD. Besides, the differences in the IS-LST relations across urban agglomeration indicate more significant enhancing effects of IS on LST across PRD than YRD and BTH. These findings help to enhance human understanding of the warming effects of urbanization or UHI at different spatial and temporal scales and is of scientific and practical merits for scientific urban planning.
Remote Sensing Image Semantic Segmentation Based on Edge Information Guidance
Authors: He Chu , Li Shenglin , Xiong Dehui , Fang Peizhang , Liao Mingsheng
Published: May 2020

Semantic segmentation is an important field for automatic processing of remote sensing image data. Existing algorithms based on Convolution Neural Network (CNN) have made rapid progress, especially the Fully Convolution Network (FCN). However, problems still exist when directly inputting remote sensing images to FCN because the segmentation result of FCN is not fine enough, and it lacks guidance for prior knowledge. To obtain more accurate segmentation results, this paper introduces edge information as prior knowledge into FCN to revise the segmentation results. Specifically, the Edge-FCN network is proposed in this paper, which uses the edge information detected by Holistically Nested Edge Detection (HED) network to correct the FCN segmentation results. The experiment results on ESAR dataset and GID dataset demonstrate the validity of Edge-FCN.
A Novel Method Based on Backscattering for Discriminating Summer Blooms of the Raphidophyte (Chattonella spp.) and the Diatom (Skeletonema spp.) Using MODIS Images in Ariake Sea, Japan
Authors: Feng Chi , Ishizaka Joji , Saitoh Katsuya , Mine Takayuki , Yamashita Hirokazu
Published: May 2020

The raphidophyte Chattonella spp. and diatom Skeletonema spp. are the dominant harmful algal species of summer blooms in Ariake Sea, Japan. A new bio-optical algorithm based on backscattering features has been developed to differentiate harmful raphidophyte blooms from diatom blooms using MODIS imagery. Bloom waters were first discriminated from other water types based on the distinct spectral shape of the remote sensing reflectance R r s (λ) data derived from MODIS. Specifically, bloom waters were discriminated by the positive value of Spectral Shape, SS (645), which arises from the R r s (λ) shoulder at 645 nm in bloom waters. Next, the higher cellular-specific backscattering coefficient, estimated from MODIS data and quasi-analytical algorithm (QAA) of raphidophyte, Chattonella spp., was utilized to discriminate it from blooms of the diatom, Skeletonema spp. A new index b b p − i n d e x ( 555 ) was calculated based on a semi-analytical bio-optical model to discriminate the two algal groups. This index, combined with a supplemental Red Band Ratio (RBR) index, effectively differentiated the two bloom types. Validation of the method was undertaken using MODIS satellite data coincident with confirmed bloom observations from local field surveys, which showed that the newly developed method, based on backscattering features, could successfully discriminate the raphidophyte Chattonella spp. from the diatom Skeletonema spp. and thus provide reliable information on the spatial distribution of harmful blooms in Ariake Sea.
Assessment of Electronic Sensing Techniques for the Rapid Identification of Alveolar Echinococcosis through Exhaled Breath Analysis
Authors: Kwiatkowski Andrzej , Chludziński Tomasz , Saidi Tarik , Welearegay Tesfalem Geremariam , Jaimes-Mogollón Aylen Lisset , El Bari Nezha , Borys Sebastian , Bouchikhi Benachir , Smulko Janusz , Ionescu Radu
Published: May 2020

Here we present a proof-of-concept study showing the potential of a chemical gas sensors system to identify the patients with alveolar echinococcosis disease through exhaled breath analysis. The sensors system employed comprised an array of three commercial gas sensors and a custom gas sensor based on WO3 nanowires doped with gold nanoparticles, optimized for the measurement of common breath volatile organic compounds. The measurement setup was designed for the concomitant measurement of both sensors DC resistance and AC fluctuations during breath samples exposure. Discriminant Function Analysis classification models were built with features extracted from sensors responses, and the discrimination of alveolar echinococcosis was estimated through bootstrap validation. The commercial sensor that detects gases such as alkane derivatives and ethanol, associated with lipid peroxidation and intestinal gut flora, provided the best classification (63.4% success rate, 66.3% sensitivity and 54.6% specificity) when sensors’ responses were individually analyzed, while the model built with the AC features extracted from the responses of the cross-reactive sensors array yielded 90.2% classification success rate, 93.6% sensitivity and 79.4% specificity. This result paves the way for the development of a noninvasive, easy to use, fast and inexpensive diagnostic test for alveolar echinococcosis diagnosis at an early stage, when curative treatment can be applied to the patients.
Multiple Electric Energy Consumption Forecasting Using a Cluster-Based Strategy for Transfer Learning in Smart Building
Authors: Le Tuong , Vo Minh Thanh , Kieu Tung , Hwang Eenjun , Rho Seungmin , Baik Sung Wook
Published: May 2020

Electric energy consumption forecasting is an interesting, challenging, and important issue in energy management and equipment efficiency improvement. Existing approaches are predictive models that have the ability to predict for a specific profile, i.e., a time series of a whole building or an individual household in a smart building. In practice, there are many profiles in each smart building, which leads to time-consuming and expensive system resources. Therefore, this study develops a robust framework for the Multiple Electric Energy Consumption forecasting (MEC) of a smart building using Transfer Learning and Long Short-Term Memory (TLL), the so-called MEC-TLL framework. In this framework, we first employ a k-means clustering algorithm to cluster the daily load demand of many profiles in the training set. In this phase, we also perform Silhouette analysis to specify the optimal number of clusters for the experimental datasets. Next, this study develops the MEC training algorithm, which utilizes a cluster-based strategy for transfer learning the Long Short-Term Memory models to reduce the computational time. Finally, extensive experiments are conducted to compare the computational time and different performance metrics for multiple electric energy consumption forecasting on two smart buildings in South Korea. The experimental results indicate that our proposed approach is capable of economical overheads while achieving superior performances. Therefore, the proposed approach can be applied effectively for intelligent energy management in smart buildings.
A Novel High-Frequency Vibration Error Estimation and Compensation Algorithm for THz-SAR Imaging Based on Local FrFT
Authors: Li Yinwei , Ding Li , Zheng Qibin , Zhu Yiming , Sheng Jialian
Published: May 2020

Compared with microwave synthetic aperture radar (SAR), terahertz SAR (THz-SAR) is easier to achieve ultrahigh-resolution image due to its higher frequency and shorter wavelength. However, higher carrier frequency makes THz-SAR image quality very sensitive to high-frequency vibration error of motion platform. Therefore, this paper proposes a novel high-frequency vibration error estimation and compensation algorithm for THz-SAR imaging based on local fractional Fourier transform (LFrFT). Firstly, the high-frequency vibration error of the motion platform is modeled as a simple harmonic motion and THz-SAR echo signal received in each range pixel can be considered as a sinusoidal frequency modulation (SFM) signal. A novel algorithm for the parameter estimation of the SFM signal based on LFrFT is proposed. The instantaneous chirp rate of the SFM signal is estimated by determining the matched order of LFrFT in a sliding small-time window and the vibration acceleration is obtained. Hence, the vibration frequency can be estimated by the spectrum analysis of estimated vibration acceleration. With the estimated vibration acceleration and vibration frequency, the SFM signal is reconstructed. Then, the corresponding THz-SAR imaging algorithm is proposed to estimate and compensate the phase error caused by the high-frequency vibration error of the motion platform and realize high-frequency vibration error estimation and compensation for THz-SAR imaging. Finally, the effectiveness of the novel algorithm proposed in this paper is demonstrated by simulation results.
A Simple High-Resolution Near-Field Probe for Microwave Non-Destructive Test and Imaging
Authors: Xie Zipeng , Li Yongjie , Sun Liguo , Wu Wentao , Cao Rui , Tao Xiaohui
Published: May 2020

Non-destructive tests working at lower microwave frequencies have large advantages of dielectric material penetrability, lower equipment cost, and lower implementation complexity. However, the resolution will become worse as the work frequencies become lower. Relying on designing the structure of high field confinement, this study realizes a simple complementary spiral resonators (CSRs)-based near-field probe for microwave non-destructive testing (NDT) and imaging around 390 MHz (λ = 769 mm) whereby very high resolution (λ/308, 2.5 mm) is achieved. By applying an ingenious structure where a short microstrip is connected to a microstrip ring to feed the CSR, the probe, that is a single-port microwave planar circuit, does not need any extra matching circuits, which has more application potential in sensor arraying compared with other microwave probes. The variation of the electric field distribution with the standoff distance (SOD) between the material under test and the probe are analyzed to reveal the operation mechanisms behind the improved sensitivity and resolution of the proposed probe. Besides, the detection abilities of the tiny defects in metal and non-metal materials are demonstrated by the related experiments. The smallest detectable crack and via in the non-metal materials and the metal materials are of a λ/1538 (0.5 mm) width, a λ/513 (1.5 mm) diameter, a λ/3846 (0.2 mm) width and a λ/513 (1.5 mm) diameter, respectively. Moreover, to further evaluate the performance of the proposed probe, the defects under skin layer in the multilayer composite materials and the defects under corrosion in the carbon steel are inspected and imaged. Due to lower work frequency, high resolution, outstanding detection abilities of tiny defects, and large potentials in sensor arraying, the proposed probe would be a good candidate for microwave NDT and imaging.