bluuebunny/crossref_metadata_2025 huggingface dataset:
Intra- and Inter-Server Smart Task Scheduling for Profit and Energy Optimization of HPC Data Centers
Authors: Mamun Sayed Ashraf , Gilday Alexander , Singh Amit Kumar , Ganguly Amlan , Merrett Geoff V. , Wang Xiaohang , Al-Hashimi Bashir M.
Published: October 2020

Servers in a data center are underutilized due to over-provisioning, which contributes heavily toward the high-power consumption of the data centers. Recent research in optimizing the energy consumption of High Performance Computing (HPC) data centers mostly focuses on consolidation of Virtual Machines (VMs) and using dynamic voltage and frequency scaling (DVFS). These approaches are inherently hardware-based, are frequently unique to individual systems, and often use simulation due to lack of access to HPC data centers. Other approaches require profiling information on the jobs in the HPC system to be available before run-time. In this paper, we propose a reinforcement learning based approach, which jointly optimizes profit and energy in the allocation of jobs to available resources, without the need for such prior information. The approach is implemented in a software scheduler used to allocate real applications from the Princeton Application Repository for Shared-Memory Computers (PARSEC) benchmark suite to a number of hardware nodes realized with Odroid-XU3 boards. Experiments show that the proposed approach increases the profit earned by 40% while simultaneously reducing energy consumption by 20% when compared to a heuristic-based approach. We also present a network-aware server consolidation algorithm called Bandwidth-Constrained Consolidation (BCC), for HPC data centers which can address the under-utilization problem of the servers. Our experiments show that the BCC consolidation technique can reduce the power consumption of a data center by up-to 37%.
Candida auris Direct Detection from Surveillance Swabs, Blood, and Urine Using a Laboratory-Developed PCR Method
Authors: Walchak Robert C. , Buckwalter Seanne P. , Zinsmaster Nicole M. , Henn Katrina M. , Johnson Katelyn M. , Koelsch Jolene M. , Herring Senait A. , Steinmetz Lory K. , Reed Katelyn A. , Barth Jean E. , Rasmusson Jenna M. , Fischer Jill L. , Snippes Vagnone Paula , Sampathkumar Priya , Wengenack Nancy L.
Published: October 2020

Candida auris is an emerging fungal pathogen with cases reported in countries around the world and in 19 states within the United States as of August 2020. The CDC has recommended that hospitals perform active surveillance upon admission for patients with the appropriate risk factors. Currently, active surveillance requires that local hospitals send surveillance swabs to a public health laboratory for analysis. In this work, a real-time PCR assay was developed for the specific detection of C. auris from surveillance swabs, blood, and urine to enable rapid detection of this pathogen. The assay uses commercially available primers and reporter probes and it was verified on the LightCycler 480 PCR platform. Contrived specimens and prospectively collected composite groin/axilla surveillance swabs were used to validate the assay. The performance of the PCR assay on surveillance swabs was also compared to a second PCR assay targeting C. auris that was performed at the Minnesota Department of Health–Public Health Laboratory (MDH-PHL). Our PCR assay is able to detect and differentiate C. auris from closely related Candida species such as C. duobushaemulonii, C. haemulonii, and C. pseudohaemulonii on the basis of melting curve temperature differences.
Comparison of Clonogenic Survival Data Obtained by Pre- and Post-Irradiation Methods
Authors: Oike Takahiro , Hirota Yuka , Dewi Maulany Darwis Narisa , Shibata Atsushi , Ohno Tatsuya
Published: October 2020

Clonogenic assays are the gold standard to measure in vitro radiosensitivity, which use two cell plating methods, before or after irradiation (IR). However, the effect of the plating method on the experimental outcome remains unelucidated. By using common cancer cell lines, here we demonstrate that pre-IR and post-IR plating methods have a negligible effect on the clonogenic assay-derived photon sensitivity as assessed by SF2, SF4, SF6, SF8, D10, or D50 (N.B. SFx indicates the survival at X Gy; Dx indicates the dose providing X% survival). These data provide important biological insight that supports inter-study comparison and integrated analysis of published clonogenic assay data regardless of the plating method used.
A ‘Wellbeing’ Paradigm: A Concept-Based Study of Body Art and Regulatory Challenges
Authors: Glover-Thomas Nicola
Published: October 2020

In this paper, I trace the changing characterisation of health and consider the evolution of health within a shifting paradigmatic landscape. I argue that understanding health now encompasses the importance of wellbeing as a key determinant of longer-term good health. I use the case study of body modification and body art to explore this further. I argue that, while body modification and body art, as a means of self-expression and empowerment, is relatively easy to access, there are critical gaps in the regulatory framework that may undermine the notion of wellbeing and individual choice. I critique the Court of Appeal’s decision in R v BM, [2018] EWCA Crim 560 which raises particular public interest concerns, but conclude that it is a missed opportunity in relation to how the law understands the promotion of ‘self’ within a model of wellbeing.
Enhanced Selective Hydrogen Permeation through Graphdiyne Membrane: A Theoretical Study
Authors: Liu Quan , Cheng Long , Liu Gongping
Published: October 2020

Graphdiyne (GDY), with uniform pores and atomic thickness, is attracting widespread attention for application in H2 separation in recent years. However, the challenge lies in the rational design of GDYs for fast and selective H2 permeation. By MD and DFT calculations, several flexible GDYs were constructed to investigate the permeation properties of four pure gas (H2, N2, CO2, and CH4) and three equimolar binary mixtures (H2/N2, H2/CO2, and H2/CH4) in this study. When the pore size is smaller than 2.1 Å, the GDYs acted as an exceptional filter for H2 with an approximately infinite H2 selectivity. Beyond the size-sieving effect, in the separation process of binary mixtures, the blocking effect arising from the strong gas–membrane interaction was proven to greatly impede H2 permeation. After understanding the mechanism, the H2 permeance of the mixtures of H2/CO2 was further increased to 2.84 × 105 GPU by reducing the blocking effect with the addition of a tiny amount of surface charges, without sacrificing the selectivity. This theoretical study provides an additional atomic understanding of H2 permeation crossing GDYs, indicating that the GDY membrane could be a potential candidate for H2 purification.
Effects of Extreme Thermal Shock on Microstructure and Mechanical Properties of Au-12Ge/Au/Ni/Cu Solder Joint
Authors: Wang Ziyi , Xue Songbai , Long Weimin , Wang Bo , Wang Jianhao , Zhang Peng
Published: October 2020

Extreme temperature change has generally been the great challenge to spacecraft electronic components, particularly in long, periodic, deep-space exploration missions. Hence, researchers have paid more attention to the reliability of component packaging materials. In this study, the microstructure evolution on the interface of Cu/Ni/Au/Au-12Ge/Au/Ni/Cu joints, as well as the effects of extreme thermal shock on mechanical properties and the fracture mode in the course of extreme thermal changes between −196 and 150 °C, have been investigated. Results revealed that the interface layers comprised of two thin layers of NiGe and Ni5Ge3 compounds after Au-12Ge solder alloy was soldered on the Au/Ni/Cu substrate. After extreme thermal shock tests, the microstructure morphology converted from scallop type to planar one due to the translation from NiGe to Ni5Ge3. Meanwhile, the thickness of interface layer hardly changed. The shear strength of the joints after 300 cycles of extreme thermal shock was 35.1 MPa, which decreased by 19.61%. The fracture location changed from the solder to solder/NiGe interface, and then to the interface of NiGe/Ni5Ge3 IMC layer. Moreover, the fracture type of the joints gradually transformed from ductile fracture mode to brittle mode during thermal shock test. Simultaneously, the formation and extension of defects, such as micro-voids and micro-cracks, were found during the process of thermal shock due to the different thermal expansion coefficient among the solder, interface layer and substrate.
Study on Deformation Characteristics and Microstructure Evolution of 2205/AH36 Bimetal Composite in a Novel Hot Forming Process
Authors: Li Zhou , Xie Haibo , Jia Fanghui , Lu Yao , Yuan Xiangqian , Jiao Sihai , Jiang Zhengyi
Published: October 2020

A new hot forming process of a hot-rolled 2205 duplex stainless/AH36 low-carbon steel bimetal composite (2205/AH36 BC) was proposed in this study, using the Gleeble 3500 thermal-mechanical simulator and hot bending tools. The deformation characteristics of 2205/AH36 BC were studied by hot tensile tests at temperatures from 950 to 1250 °C and strain rates ranging from 0.01 to 1 s−1. The tensile temperature has a great influence on the peak flow stress of the bimetal composite. The main microstructure evolution mechanisms, including dynamic recovery (DRV) and dynamic recrystallization (DRX), changed with the deformation temperatures. The different strain rates and the change of strain rates during the deformation process have an influence on the flow behavior of the bimetal composite. During the hot bending process, qualified parts could be formed successfully without obvious cracks in the interfacial zone. Phase and grain orientation spread (GOS) maps of specimens after hot tensile and forming tests were obtained by the electron backscatter diffraction (EBSD) technique to study the microstructure evolution, respectively. It is found that the effect of the working temperature on microstructure evolution is larger than that of the stacking sequence for 2205/AH36 BC. The considerable geometrically necessary dislocation (GND) accumulation occurs around the interface of 2205/AH36 BC under all imposed working conditions after the hot bending process, due to the interfacial micro-defects and complex stress states.
Power Management IC for a Dual-Input-Triple-Output Energy Harvester
Authors: Mui Kai-Meng , Khaw Mei-Kum , Mohd-Yasin Faisal
Published: October 2020

We present the design of a power management integrated circuit that processes harvested energy from radio frequency waves and piezoelectric vibrations. The rectification of piezoelectric and RF sources has a power conversion efficiency (PCE) of 87.73% and 74.70%, respectively. The asynchronous and microcontroller-less integrated circuit (IC) is designed to be low power, so the bulk of the harvested energy goes to three loads. The output peak powers of 111 μW, 156 μW, and 128 μW will be sufficient to run small devices for RF communication systems.
An Efficient Method for Isolation of Plasmid DNA for Transfection of Mammalian Cell Cultures
Authors: Kachkin Daniel V. , Khorolskaya Julia I. , Ivanova Julia S. , Rubel Aleksandr A.
Published: October 2020

In this article, we present several protocols that describe the steps from cloning and obtaining a large amount of pure plasmid DNA to generation of lentiviruses based on these constructs. The protocols have been worked out on human cell culture HEK293T but can be adapted for other cell cultures. This protocol was designed to be simple to execute and cheap since it requires only materials and consumables widely available in molecular laboratories, such as salts, alcohols, etc., and no complicated laboratory equipment. These protocols are highly effective and can be performed in any standard molecular biology laboratory.
One-Pot Hydrothermal Synthesis of La-Doped ZnIn2S4 Microspheres with Improved Visible-Light Photocatalytic Performance
Authors: Jia Tiekun , Liu Ming , Zheng Chunyang , Long Fei , Min Zhiyu , Fu Fang , Yu Dongsheng , Li Jili , Lee Joong Hee , Kim Nam Hoon
Published: October 2020

Impurity element doping is extensively taken as one of the most efficient strategies to regulate the electronic structure as well as the rate of photogenerated charge separation of photocatalysts. Herein, a one-pot hydrothermal synthesis process was exploited to obtain La-doped ZnIn2S4 microspheres, aiming at gaining insight into the role that doping ions played in the improvement of pollutant photodegradation. Systematical characterization means, comprising of X–ray photoelectron spectroscopy (XPS), ultraviolet–visible (UV–vis) diffuse reflection spectroscopy and Raman spectra, combination with high-resolution transmission electron microscopy (HRTEM), were employed to in depth reveal the concomitancy of La ions and ZnIn2S4 crystal lattice. The results showed that the La-doped ZnIn2S4 samples exhibited a slightly wider and stronger spectral absorption than pristine ZnIn2S4; and the specific surface area of doped ZnIn2S4 samples was a bit larger. The La-doped ZnIn2S4 electrodes showed improved photocurrent response, and the photocurrent density reached a maximum value at La content of 1.5 wt%. As expected, La-doped ZnIn2S4 samples exhibited a remarkable enhancement of photocatalytic behaviour toward the photodegradation of tetracycline hydrochloride (TCH) and methyl orange (MO). The prominently enhanced photoactivity of doped ZnIn2S4 samples was due to the synergistic effect of the elevated visible-light absorption ability and effective photogenerated charge carriers’ separation.
Ultrawide Bandgap and High Sensitivity of a Plasmonic Metal-Insulator-Metal Waveguide Filter with Cavity and Baffles
Authors: Chou Chau Yuan-Fong , Chou Chao Chung-Ting , Huang Hung Ji , Kooh Muhammad Raziq Rahimi , Kumara Narayana Thotagamuge Roshan Nilantha , Lim Chee Ming , Chiang Hai-Pang
Published: October 2020

A plasmonic metal-insulator-metal waveguide filter consisting of one rectangular cavity and three silver baffles is numerically investigated using the finite element method and theoretically described by the cavity resonance mode theory. The proposed structure shows a simple shape with a small number of structural parameters that can function as a plasmonic sensor with a filter property, high sensitivity and figure of merit, and wide bandgap. Simulation results demonstrate that a cavity with three silver baffles could significantly affect the resonance condition and remarkably enhance the sensor performance compared to its counterpart without baffles. The calculated sensitivity (S) and figure of merit (FOM) in the first mode can reach 3300.00 nm/RIU and 170.00 RIU−1. Besides, S and FOM values can simultaneously get above 2000.00 nm/RIU and 110.00 RIU−1 in the first and second modes by varying a broad range of the structural parameters, which are not attainable in the reported literature. The proposed structure can realize multiple modes operating in a wide wavelength range, which may have potential applications in the on-chip plasmonic sensor, filter, and other optical integrated circuits.
Synthesis of Citrate-Stabilized Silver Nanoparticles Modified by Thermal and pH Preconditioned Tannic Acid
Authors: La Spina Rita , Mehn Dora , Fumagalli Francesco , Holland Margaret , Reniero Fabiano , Rossi François , Gilliland Douglas
Published: October 2020

Silver nanoparticles (AgNPs) may be synthesized by many different methods, with those based on the thermal reduction of silver salts by citric acid or citric acid/tannic acid being amongst the most commonly used. These methods, although widely used and technically simple, can produce particles in which the size, polydispersivity and morphology can vary greatly. In this work nearly mono-dispersed spherical AgNPs have been synthesized via a one-step reduction method by using sodium citrate and varying quantities of Tannic Acid (TA), which was thermally conditioned prior to use in the growth process. It was found that the final size can be further tailored by controlling the amount of TA and the thermal conditioning of the TA at 60 °C at different time points, which changes the size and polydispersivity of AgNPs. To better understand the origin of this effect, optical spectroscopic analysis and 1H NMR of the TA following mild thermal conditioning of the solution have been done. Comparison of thermally conditioned TA and TA exposed to basic pH shows that similar chemical modifications occur and consequently produce similar effects on growth when used in the synthesis of AgNPs. It is proposed that thermal preconditioning of the TA introduces either chemical or structural changes, which decrease the final particle size under a given total silver content.
Enhancing Förster Resonance Energy Transfer (FRET) Efficiency of Titania–Lanthanide Hybrid Upconversion Nanomaterials by Shortening the Donor–Acceptor Distance
Authors: Lin Syue-Liang , Chen Han-Chun , Chang Cheng Allen
Published: October 2020

Several robust titania (TiO2) coated core/multishell trivalent lanthanide (Ln) upconversion nanoparticles (UCNPs) hybrid architecture designs have been reported for use in photodynamic therapy (PDT) against cancer, utilizing the near-infrared (NIR) excited energy down-shifting and up-conversion chain of Nd3+ (λ793-808 nm) → Yb3+ (λ980 nm) → Tm3+(λ475 nm) → TiO2 to produce reactive oxygen species (ROS) for deep tissue-penetrating oxidative cytotoxicity, e.g., NaLnF4:Yb,Tm (Ln = Y, Gd). Herein, we demonstrate that by doping the Tm3+ emitter ions in the outer shell and the Nd3+ sensitizer ions in the core, the newly designed NaYF4:Nd,Yb@Yb@Yb,Tm@TiO2 hybrid UCNPs exert more ROS production than the reference NaYF4:Yb,Tm@Yb@Nd,Yb@ TiO2 with the Tm3+ ions in the core and the Nd3+ ions in the outer shell, upon 793 nm laser irradiation, primarily due to the shortening of the Tm3+-TiO2 distance of the former with greater Förster resonance energy transfer (FRET) efficiency. After coating with polyallylamine hydrochloride (PAH)/polyethylene glycol folate (PEG-FA), the resulting NaYF4:Nd,Yb@Yb@Yb,Tm@TiO2-PAH-PEG-FA hybrid nanocomposites could be internalized in MDA-MB-231 cancer cells, which also show low dark cytotoxicity and effective photocytotoxicity upon 793 nm excitation. These nanocomposites could be further optimized and are potentially good candidates as nanotheranostics, as well as for other light-conversion applications.
Mesoporous Carbons from Polysaccharides and Their Use in Li-O2 Batteries
Authors: Uriburu-Gray María , Pinar-Serrano Aránzazu , Cavus Gokhan , Knipping Etienne , Aucher Christophe , Conesa-Cabeza Aleix , Satti Amro , Amantia David , Martínez-Crespiera Sandra
Published: October 2020

Previous studies have demonstrated that the mesoporosity of carbon material obtained by the Starbon® process from starch-formed by amylose and amylopectin can be tuned by controlling this ratio (the higher the amylose, the higher the mesoporosity). This study shows that starch type can also be an important parameter to control this mesoporosity. Carbons with controlled mesoporosity (Vmeso from 0.1–0.7 cm3/g) have been produced by the pre-mixing of different starches using an ionic liquid (IL) followed by a modified Starbon® process. The results show that the use of starch from corn and maize (commercially available Hylon VII with maize, respectively) is the better combination to increase the mesopore volume. Moreover, “low-cost” mesoporous carbons have been obtained by the direct carbonization of the pre-treated starch mixtures with the IL. In all cases, the IL can be recovered and reused, as demonstrated by its recycling up to three times. Furthermore, and as a comparison, chitosan has been also used as a precursor to obtain N-doped mesoporous carbons (5.5 wt% N) with moderate mesoporosity (Vmeso = 0.43 cm3/g). The different mesoporous carbons have been tested as cathode components in Li-O2 batteries and it is shown that a higher carbon mesoporosity, produced from starch precursor, or the N-doping, produced from chitosan precursor, increase the final battery cell performance (specific capacity and cycling).
A 3 Year Longitudinal Prospective Review Examining the Dietary Profile and Contribution Made by Special Low Protein Foods to Energy and Macronutrient Intake in Children with Phenylketonuria
Authors: Daly Anne , Evans Sharon , Pinto Alex , Ashmore Catherine , Rocha Júlio César , MacDonald Anita
Published: October 2020

The nutritional composition of special low protein foods (SLPFs) is controlled under EU legislation for ‘Foods for Special Medical Purposes (FSMP)’. They are designed to meet the energy needs of patients unable to eat a normal protein containing diet. In phenylketonuria (PKU), the macronutrient contribution of SLPFs has been inadequately examined. Aim: A 3-year longitudinal prospective study investigating the contribution of SLPFs to the macronutrient intake of children with early treated PKU. Methods: 48 children (27 boys) with a mean recruitment age of 9.3 y were studied. Semi-quantitative dietary assessments and food frequency questionnaires (FFQ) were collected three to four times/year for 3 years. Results: The mean energy intake provided by SLPFs was 33% (SD ± 8), and this figure was 42% (SD ± 13) for normal food and 21% (SD ± 5) for protein substitutes (PS). SLPFs supplied a mean intake of 40% carbohydrate (SD ± 10), 51% starch (SD ± 18), 21% sugar (SD ± 8), and 38% fat (SD ± 13). Fibre intake met 83% of the Scientific Advisory Committee on Nutrition (SACN) reference value, with 50% coming from SLPFs with added gums and hydrocolloids. Low protein bread, pasta and milk provided the highest energy contribution, and the intake of sweet SLPFs (e.g., biscuits, cakes, and chocolate) was minimal. Children averaged three portions fruit/vegetable daily, and children aged ≥ 12 y had irregular meal patterns. Conclusion: SLPFs provide essential energy in phenylalanine restricted diets. Optimising the nutritional quality of SLPFs deserves more attention.
Novel Ensemble of Multivariate Adaptive Regression Spline with Spatial Logistic Regression and Boosted Regression Tree for Gully Erosion Susceptibility
Authors: Roy Paramita , Chandra Pal Subodh , Arabameri Alireza , Chakrabortty Rabin , Pradhan Biswajeet , Chowdhuri Indrajit , Lee Saro , Tien Bui Dieu
Published: October 2020

The extreme form of land degradation through different forms of erosion is one of the major problems in sub-tropical monsoon dominated region. The formation and development of gullies is the dominant form or active process of erosion in this region. So, identification of erosion prone regions is necessary for escaping this type of situation and maintaining the correspondence between different spheres of the environment. The major goal of this study is to evaluate the gully erosion susceptibility in the rugged topography of the Hinglo River Basin of eastern India, which ultimately contributes to sustainable land management practices. Due to the nature of data instability, the weakness of the classifier andthe ability to handle data, the accuracy of a single method is not very high. Thus, in this study, a novel resampling algorithm was considered to increase the robustness of the classifier and its accuracy. Gully erosion susceptibility maps have been prepared using boosted regression trees (BRT), multivariate adaptive regression spline (MARS) and spatial logistic regression (SLR) with proposed resampling techniques. The re-sampling algorithm was able to increase the efficiency of all predicted models by improving the nature of the classifier. Each variable in the gully inventory map was randomly allocated with 5-fold cross validation, 10-fold cross validation, bootstrap and optimism bootstrap, while each consisted of 30% of the database. The ensemble model was tested using 70% and validated with the other 30% using the K-fold cross validation (CV) method to evaluate the influence of the random selection of training and validation database. Here, all resampling methods are associated with higher accuracy, but SLR bootstrap optimism is more optimal than any other methods according to its robust nature. The AUC values of BRT optimism bootstrap, MARS optimism bootstrap and SLR optimism bootstrap are 87.40%, 90.40% and 90.60%, respectively. According to the SLR optimism bootstrap, the 107,771 km2 (27.51%) area of this region is associated with a very high to high susceptible to gully erosion. This potential developmental area of the gully was found primarily in the Hinglo River Basin, where lateral exposure was mainly observed with scarce vegetation. The outcome of this work can help policy-makers to implement remedial measures to minimize the damage caused by erosion of the gully.
What Can We Learn from the CloudSat Radiometric Mode Observations of Snowfall over the Ice-Free Ocean?
Authors: Battaglia Alessandro , Panegrossi Giulia
Published: October 2020

The quantification of global snowfall by the current observing system remains challenging, with the CloudSat 94 GHz Cloud Profiling Radar (CPR) providing the current state-of-the-art snow climatology, especially at high latitudes. This work explores the potential of the novel Level-2 CloudSat 94 GHz Brightness Temperature Product (2B-TB94), developed in recent years by processing the noise floor data contained in the 1B-CPR product; the focus of the study is on the characterization of snow systems over the ice-free ocean, which has well constrained emissivity and backscattering properties. When used in combination with the path integrated attenuation (PIA), the radiometric mode can provide crucial information on the presence/amount of supercooled layers and on the contribution of the ice to the total attenuation. Radiative transfer simulations show that the location of the supercooled layers and the snow density are important factors affecting the warming caused by supercooled emission and the cooling induced by ice scattering. Over the ice-free ocean, the inclusion of the 2B-TB94 observations to the standard CPR observables (reflectivity profile and PIA) is recommended, should more sophisticated attenuation corrections be implemented in the snow CloudSat product to mitigate its well-known underestimation at large snowfall rates. Similar approaches will also be applicable to the upcoming EarthCARE mission. The findings of this paper are relevant for the design of future missions targeting precipitation in the polar regions.
The Dimming of Lights in India during the COVID-19 Pandemic
Authors: Ghosh Tilottama , Elvidge Christopher D. , Hsu Feng-Chi , Zhizhin Mikhail , Bazilian Morgan
Published: October 2020

The monthly Suomi National Polar-orbiting (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day–Night Band (DNB) composite reveals the dimming of lights as an effect of the lockdown enforced by the government of India in response to the COVID-19 pandemic. The changes in lighting are examined by creating difference maps of a pre-pandemic pair and comparing it with two pandemic pairs. The visual raster difference maps are substantiated with quantitative analysis showing the proportion of population affected by the changes in the lighting brightness levels. In the pre-pandemic images of February and March 2019, 60% of the population lived in administrative units that became brighter in March 2019. However, in the first pandemic pair, 87% of the population lived in administrative units that became dimmer in March 2020 after the lockdown in comparison to February 2020. The nightly DNB profile at the airport in Delhi illustrate how the dimming of lights coincide with the date of the onset of the lockdown (in March 2020). The study shows the usefulness of the DNB nightly and monthly composites in examining economic impacts of the pandemic as countries throughout the world go through economic declines and move towards recovery.
A Novel Method for River Bank Detection from Landsat Satellite Data: A Case Study in the Vietnamese Mekong Delta
Authors: Binh Doan Van , Wietlisbach Basil , Kantoush Sameh , Loc Ho Huu , Park Edward , Cesare Giovanni de , Cuong Do Huy , Tung Nguyen Xuan , Sumi Tetsuya
Published: October 2020

River bank (RB) erosion is a global issue affecting livelihoods and properties of millions of people. However, it has not received enough attention in the Vietnamese Mekong Delta (VMD), i.e., the world’s third largest delta, compared to salinity intrusion and flooding. There have been several studies examining RB and coastal erosion in the VMD using remotely sensed satellite data, but the applied methodology was not adequately validated. Therefore, we developed a novel SRBED (Spectral RB Erosion Detection) method, in which the M-AMERL (Modified Automated Method for Extracting Rivers and Lakes) is proposed, and a new RB change detection algorithm using Landsat data. The results show that NDWI (Normalized Difference Water Index) and MNDWI (Modified Normalized Difference Water Index) using the M-AMERL algorithm (i.e., NDWIM-AMERL, MNDWIM-AMERL) perform better than other indices. Furthermore, the NDWIM-AMERL; SMA (i.e., NDWIM-AMERL using the SMA (Spectral Mixture Analysis) algorithm) is the best RB extraction method in the VMD. The NDWIM-AMERL; SMA performs better than the MNDWI, NDVI (Normalized Difference Vegetation Index), and WNDWI (Weighted Normalized Difference Water Index) indices by 35–41%, 70% and 30%, respectively. Moreover, the NDVI index is not recommended for assessing RB changes in the delta. Applying the developed SRBED method and RB change detection algorithm, we estimated a net erosion area of the RB of –1.5 km2 from 2008 to 2014 in the Tien River from Tan Chau to My Thuan, with a mean erosion width of –2.64 m and maximum erosion widths exceeding 60 m in places. Our advanced method can be applied in other river deltas having similar characteristics, and the results from our study are helpful in future studies in the VMD.
A Hybrid Data Balancing Method for Classification of Imbalanced Training Data within Google Earth Engine: Case Studies from Mountainous Regions
Authors: Naboureh Amin , Li Ainong , Bian Jinhu , Lei Guangbin , Amani Meisam
Published: October 2020

Distribution of Land Cover (LC) classes is mostly imbalanced with some majority LC classes dominating against minority classes in mountainous areas. Although standard Machine Learning (ML) classifiers can achieve high accuracies for majority classes, they largely fail to provide reasonable accuracies for minority classes. This is mainly due to the class imbalance problem. In this study, a hybrid data balancing method, called the Partial Random Over-Sampling and Random Under-Sampling (PROSRUS), was proposed to resolve the class imbalance issue. Unlike most data balancing techniques which seek to fully balance datasets, PROSRUS uses a partial balancing approach with hundreds of fractions for majority and minority classes to balance datasets. For this, time-series of Landsat-8 and SRTM topographic data along with various spectral indices and topographic data were used over three mountainous sites within the Google Earth Engine (GEE) cloud platform. It was observed that PROSRUS had better performance than several other balancing methods and increased the accuracy of minority classes without a reduction in overall classification accuracy. Furthermore, adopting complementary information, particularly topographic data, considerably increased the accuracy of minority classes in mountainous areas. Finally, the obtained results from PROSRUS indicated that every imbalanced dataset requires a specific fraction(s) for addressing the class imbalance problem, because different datasets contain various characteristics.
VddNet: Vine Disease Detection Network Based on Multispectral Images and Depth Map
Authors: Kerkech Mohamed , Hafiane Adel , Canals Raphael
Published: October 2020

Vine pathologies generate several economic and environmental problems, causing serious difficulties for the viticultural activity. The early detection of vine disease can significantly improve the control of vine diseases and avoid spread of virus or fungi. Currently, remote sensing and artificial intelligence technologies are emerging in the field of precision agriculture. They offer interesting potential for crop disease management. However, despite the advances in these technologies, particularly deep learning technologies, many problems still present considerable challenges, such as semantic segmentation of images for disease mapping. In this paper, we present a new deep learning architecture called Vine Disease Detection Network (VddNet). It is based on three parallel auto-encoders integrating different information (i.e., visible, infrared and depth). Then, the decoder reconstructs and retrieves the features, and assigns a class to each output pixel. An orthophotos registration method is also proposed to align the three types of images and enable the processing by VddNet. The proposed architecture is assessed by comparing it with the most known architectures: SegNet, U-Net, DeepLabv3+ and PSPNet. The deep learning architectures were trained on multispectral data from an unmanned aerial vehicle (UAV) and depth map information extracted from 3D processing. The results of the proposed architecture show that the VddNet architecture achieves higher scores than the baseline methods. Moreover, this study demonstrates that the proposed method has many advantages compared to methods that directly use the UAV images.
The Relationship between Surface Displacement and Groundwater Level Change and Its Hydrogeological Implications in an Alluvial Fan: Case Study of the Choshui River, Taiwan
Authors: Lu Chiao-Yin , Hu Jyr-Ching , Chan Yu-Chang , Su Yuan-Fong , Chang Chih-Hsin
Published: October 2020

Balancing the demand of groundwater resources and the mitigation of land subsidence is particularly important, yet challenging, in populated alluvial fan areas. In this study, we combine multiple monitoring data derived from Multi-Temporal InSAR (MTI), GNSS (Global Navigation Satellite System), precise leveling, groundwater level, and compaction monitoring wells, in order to analyze the relationship between surface displacement and groundwater level change within the alluvial fan of the Choshui River in Taiwan. Our combined time-series analyses suggest, in a yearly time scale, that groundwater level increases with the vertical surface displacement when the effect of pore water pressure dominates. Conversely, this relationship is negative when the effect of water-mass loading predominates over pore water pressure. However, the correlation between the vertical surface displacement and the groundwater level change is consistently positive over the time scale of two decades. It is interpreted that the alluvial fan sequence in the subsurface is not fully elastic, and compaction is greater than rebound in this process. These findings were not well reported and discussed by previous studies because of insufficient monitoring data and analyses. Understanding the combined effect of groundwater level change and vertical surface displacement is very helpful for management of land subsidence and usage of groundwater resources. The spatial and temporal integration of multi-sensors can be applied to overcome the limitations associated with the single technique and provides further insights into land surface changes, particularly in highly populated alluvial fan areas.
Intelligent Ship Detection in Remote Sensing Images Based on Multi-Layer Convolutional Feature Fusion
Authors: Zhang Yulian , Guo Lihong , Wang Zengfa , Yu Yang , Liu Xinwei , Xu Fang
Published: October 2020

Intelligent detection and recognition of ships from high-resolution remote sensing images is an extraordinarily useful task in civil and military reconnaissance. It is difficult to detect ships with high precision because various disturbances are present in the sea such as clouds, mist, islands, coastlines, ripples, and so on. To solve this problem, we propose a novel ship detection network based on multi-layer convolutional feature fusion (CFF-SDN). Our ship detection network consists of three parts. Firstly, the convolutional feature extraction network is used to extract ship features of different levels. Residual connection is introduced so that the model can be designed very deeply, and it is easy to train and converge. Secondly, the proposed network fuses fine-grained features from shallow layers with semantic features from deep layers, which is beneficial for detecting ship targets with different sizes. At the same time, it is helpful to improve the localization accuracy and detection accuracy of small objects. Finally, multiple fused feature maps are used for classification and regression, which can adapt to ships of multiple scales. Since the CFF-SDN model uses a pruning strategy, the detection speed is greatly improved. In the experiment, we create a dataset for ship detection in remote sensing images (DSDR), including actual satellite images from Google Earth and aerial images from electro-optical pod. The DSDR dataset contains not only visible light images, but also infrared images. To improve the robustness to various sea scenes, images under different scales, perspectives and illumination are obtained through data augmentation or affine transformation methods. To reduce the influence of atmospheric absorption and scattering, a dark channel prior is adopted to solve atmospheric correction on the sea scenes. Moreover, soft non-maximum suppression (NMS) is introduced to increase the recall rate for densely arranged ships. In addition, better detection performance is observed in comparison with the existing models in terms of precision rate and recall rate. The experimental results show that the proposed detection model can achieve the superior performance of ship detection in optical remote sensing image.
A Novel Horizon Picking Method on Sub-Bottom Profiler Sonar Images
Authors: Li Shaobo , Zhao Jianhu , Zhang Hongmei , Bi Zijun , Qu SiHeng
Published: October 2020

Traditional manual horizon picking is time-consuming and laborious, while automatic picking methods often suffer from the limited scope of their applications and the discontinuity of picked results. In this paper, we propose a novel method for automatic horizon picking from sub-bottom profiles (SBP) by an improved filtering algorithm. First, a clear and fine SBP image is formed using an intensity transformation method. On this basis, a novel filtering method is proposed by improving the multi-scale enhancement filtering algorithm to obtain clear horizons from an SBP image. The improvement is performed by applying a vertical suppression weighting term based on the form of logistic function, which is constructed by using the eigenvectors from the Hessian matrix. Then, the filtered image is segmented using a threshold method, and the horizon points in the SBP image are picked. After that, a horizon linking method is applied, which uses the horizon directions to refine the picked horizon points. The proposed method has been verified experimentally, and accurate and continuous horizons were obtained. Finally, the proposed method is discussed and some conclusions are drawn.
Comparison of Backpack, Handheld, Under-Canopy UAV, and Above-Canopy UAV Laser Scanning for Field Reference Data Collection in Boreal Forests
Authors: Hyyppä Eric , Yu Xiaowei , Kaartinen Harri , Hakala Teemu , Kukko Antero , Vastaranta Mikko , Hyyppä Juha
Published: October 2020

In this work, we compared six emerging mobile laser scanning (MLS) technologies for field reference data collection at the individual tree level in boreal forest conditions. The systems under study were an in-house developed AKHKA-R3 backpack laser scanner, a handheld Zeb-Horizon laser scanner, an under-canopy UAV (Unmanned Aircraft Vehicle) laser scanning system, and three above-canopy UAV laser scanning systems providing point clouds with varying point densities. To assess the performance of the methods for automated measurements of diameter at breast height (DBH), stem curve, tree height and stem volume, we utilized all of the six systems to collect point cloud data on two 32 m-by-32 m test sites classified as sparse (n = 42 trees) and obstructed (n = 43 trees). To analyze the data collected with the two ground-based MLS systems and the under-canopy UAV system, we used a workflow based on our recent work featuring simultaneous localization and mapping (SLAM) technology, a stem arc detection algorithm, and an iterative arc matching algorithm. This workflow enabled us to obtain accurate stem diameter estimates from the point cloud data despite a small but relevant time-dependent drift in the SLAM-corrected trajectory of the scanner. We found out that the ground-based MLS systems and the under-canopy UAV system could be used to measure the stem diameter (DBH) with a root mean square error (RMSE) of 2–8%, whereas the stem curve measurements had an RMSE of 2–15% that depended on the system and the measurement height. Furthermore, the backpack and handheld scanners could be employed for sufficiently accurate tree height measurements (RMSE = 2–10%) in order to estimate the stem volumes of individual trees with an RMSE of approximately 10%. A similar accuracy was obtained when combining stem curves estimated with the under-canopy UAV system and tree heights extracted with an above-canopy flying laser scanning unit. Importantly, the volume estimation error of these three MLS systems was found to be of the same level as the error corresponding to manual field measurements on the two test sites. To analyze point cloud data collected with the three above-canopy flying UAV systems, we used a random forest model trained on field reference data collected from nearby plots. Using the random forest model, we were able to estimate the DBH of individual trees with an RMSE of 10–20%, the tree height with an RMSE of 2–8%, and the stem volume with an RMSE of 20–50%. Our results indicate that ground-based and under-canopy MLS systems provide a promising approach for field reference data collection at the individual tree level, whereas the accuracy of above-canopy UAV laser scanning systems is not yet sufficient for predicting stem attributes of individual trees for field reference data with a high accuracy.
The Use of Remotely Sensed Data and Polish NFI Plots for Prediction of Growing Stock Volume Using Different Predictive Methods
Authors: Hawryło Paweł , Francini Saverio , Chirici Gherardo , Giannetti Francesca , Parkitna Karolina , Krok Grzegorz , Mitelsztedt Krzysztof , Lisańczuk Marek , Stereńczak Krzysztof , Ciesielski Mariusz , Wężyk Piotr , Socha Jarosław
Published: October 2020

Forest growing stock volume (GSV) is an important parameter in the context of forest resource management. National Forest Inventories (NFIs) are routinely used to estimate forest parameters, including GSV, for national or international reporting. Remotely sensed data are increasingly used as a source of auxiliary information for NFI data to improve the spatial precision of forest parameter estimates. In this study, we combine data from the NFI in Poland with satellite images of Landsat 7 and 3D point clouds collected with airborne laser scanning (ALS) technology to develop predictive models of GSV. We applied an area-based approach using 13,323 sample plots measured within the second cycle of the NFI in Poland (2010–2014) with poor positional accuracy from several to 15 m. Four different predictive approaches were evaluated: multiple linear regression, k-Nearest Neighbours, Random Forest and Deep Learning fully connected neural network. For each of these predictive methods, three sets of predictors were tested: ALS-derived, Landsat-derived and a combination of both. The developed models were validated at the stand level using field measurements from 360 reference forest stands. The best accuracy (RMSE% = 24.2%) and lowest systematic error (bias% = −2.2%) were obtained with a deep learning approach when both ALS- and Landsat-derived predictors were used. However, the differences between the evaluated predictive approaches were marginal when using the same set of predictor variables. Only a slight increase in model performance was observed when adding the Landsat-derived predictors to the ALS-derived ones. The obtained results showed that GSV can be predicted at the stand level with relatively low bias and reasonable accuracy for coniferous species, even using field sample plots with poor positional accuracy for model development. Our findings are especially important in the context of GSV prediction in areas where NFI data are available but the collection of accurate positions of field plots is not possible or justified because of economic reasons.
The Continuity MODIS-VIIRS Cloud Mask
Authors: Frey Richard A. , Ackerman Steven A. , Holz Robert E. , Dutcher Steven , Griffith Zach
Published: October 2020

This paper introduces the Continuity Moderate Resolution Imaging Spectroradiometer (MODIS)-Visible Infrared Imaging Radiometer Suite (VIIRS) Cloud Mask (MVCM), a cloud detection algorithm designed to facilitate continuity in cloud detection between the MODIS (Moderate Resolution Imaging Spectroradiometer) on the Aqua and Terra platforms and the series of VIIRS (Visible Infrared Imaging Radiometer Suite) instruments, beginning with the Soumi National Polar-orbiting Partnership (SNPP) spacecraft. It is based on the MODIS cloud mask that has been operating since 2000 with the launch of the Terra spacecraft (MOD35) and continuing in 2002 with Aqua (MYD35). The MVCM makes use of fourteen spectral bands that are common to both MODIS and VIIRS so as to create consistent cloud detection between the two instruments and across the years 2000–2020 and beyond. Through comparison data sets, including collocated Aqua MODIS and Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) from the A-Train, this study was designed to assign statistical consistency benchmarks between the MYD35 and MVCM cloud masks. It is shown that the MVCM produces consistent cloud detection results between Aqua MODIS, SNPP VIIRS, and NOAA-20 VIIRS and that the quality is comparable to the standard Aqua MODIS cloud mask. Globally, comparisons with collocated CALIOP lidar show combined clear and cloudy sky hit rates of 88.2%, 87.5%, 86.8%, and 86.8% for MYD35, MVCM Aqua MODIS, MVCM SNPP VIIRS, and MVCM NOAA-20 VIIRS, respectively, for June through until August, 2018. For the same months and in the same order for 60S–60N, hit rates are 90.7%, 90.5%, 90.1%, and 90.3%. From the time series constructed from gridded daily means of 60S–60N cloud fractions, we found that the mean day-to-day cloud fraction differences/standard deviations in percent to be 0.68/0.55, 0.94/0.64, −0.20/0.50, and 0.44/0.82 for MVCM Aqua MODIS-MVCM SNPP VIIRS day and night, and MVCM NOAA-20 VIIRS-MVCM SNPP VIIRS day and night, respectively. It is seen that the MODIS and VIIRS 1.38 µm cirrus detection bands perform similarly but with MODIS detecting slightly more clouds in the middle to high levels of the troposphere and the VIIRS detecting more in the upper troposphere above 16 km. In the Arctic, MVCM Aqua MODIS and SNPP VIIRS reported cloud fraction differences of 0–3% during the mid-summer season and −3–4% during the mid-winter.