bluuebunny/crossref_metadata_2025 huggingface dataset:
Role of the dew water on the ground surface in HONO distribution: a case measurement in Melpitz
Authors: Ren Yangang , Stieger Bastian , Spindler Gerald , Grosselin Benoit , Mellouki Abdelwahid , Tuch Thomas , Wiedensohler Alfred , Herrmann Hartmut
Published: November 2020

Abstract. To characterize the role of dew water for the ground surface HONO distribution, nitrous acid (HONO) measurements with a Monitor for AeRosols and Gases in ambient Air (MARGA) and a LOng Path Absorption Photometer (LOPAP)
instrument were performed at the Leibniz Institute
for Tropospheric Research (TROPOS) research site in Melpitz, Germany, from 19 to 29 April 2018. The dew water was also collected and analyzed
from 8 to 14 May 2019 using a glass sampler. The high time
resolution of HONO measurements showed characteristic diurnal variations
that revealed that (i) vehicle emissions are a minor source of HONO at Melpitz
station; (ii) the heterogeneous conversion of NO2 to HONO on the ground surface
dominates HONO production at night; (iii) there is significant nighttime
loss of HONO with a sink strength of 0.16±0.12 ppbv h−1; and (iv) dew water with mean NO2- of 7.91±2.14 µg m−2
could serve as a temporary HONO source in the morning when the dew droplets
evaporate. The nocturnal observations of HONO and NO2 allowed the direct
evaluation of the ground uptake coefficients for these species at night:
γNO2→HONO=2.4×10-7 to 3.5×10-6, γHONO,ground=1.7×10-5 to
2.8×10-4. A chemical model demonstrated that HONO deposition
to the ground surface at night was 90 %–100 % of the calculated unknown HONO
source in the morning. These results suggest that dew water on the ground
surface was controlling the temporal HONO distribution rather than
straightforward NO2–HONO conversion. This can strongly enhance the OH
reactivity throughout the morning time or in other planted areas that provide a large
amount of ground surface based on the OH production rate calculation.
Long-term observational constraints of organic aerosol dependence on inorganic species in the southeast US
Authors: Zheng Yiqi , Thornton Joel A. , Ng Nga Lee , Cao Hansen , Henze Daven K. , McDuffie Erin E. , Hu Weiwei , Jimenez Jose L. , Marais Eloise A. , Edgerton Eric , Mao Jingqiu
Published: November 2020

Abstract. Organic aerosol (OA), with a large biogenic fraction in the summertime southeast US, adversely impacts air quality and human health. Stringent air
quality controls have recently reduced anthropogenic pollutants including sulfate, whose impact on OA remains unclear. Three filter measurement
networks provide long-term constraints on the sensitivity of OA to changes in inorganic species, including sulfate and ammonia. The 2000–2013
summertime OA decreases by 1.7 % yr−1–1.9 % yr−1 with little month-to-month variability, while sulfate
declines rapidly with significant monthly difference in the early 2000s. In contrast, modeled OA from a chemical-transport model (GEOS-Chem) decreases
by 4.9 % yr−1 with much larger monthly variability, largely due to the predominant role of acid-catalyzed reactive uptake of
epoxydiols (IEPOX) onto sulfate. The overestimated modeled OA dependence on sulfate can be improved by implementing a coating effect and assuming
constant aerosol acidity, suggesting the needs to revisit IEPOX reactive uptake in current models. Our work highlights the importance of secondary
OA formation pathways that are weakly dependent on inorganic aerosol in a region that is heavily influenced by both biogenic and anthropogenic
emissions.
Employing airborne radiation and cloud microphysics observations to improve cloud representation in ICON at kilometer-scale resolution in the Arctic
Authors: Kretzschmar Jan , Stapf Johannes , Klocke Daniel , Wendisch Manfred , Quaas Johannes
Published: November 2020

Abstract. Clouds play a potentially important role in Arctic climate change but are poorly represented in current atmospheric models across scales. To
improve the representation of Arctic clouds in models, it is necessary to compare models to observations to consequently reduce this
uncertainty. This study compares aircraft observations from the Arctic CLoud Observations Using airborne measurements during polar Day (ACLOUD)
campaign around Svalbard, Norway, in May–June 2017 and simulations using the ICON (ICOsahedral Non-hydrostatic) model in its numerical weather
prediction (NWP) setup at 1.2 km horizontal resolution. By comparing measurements of solar and terrestrial irradiances during ACLOUD
flights to the respective properties in ICON, we showed that the model systematically overestimates the transmissivity of the mostly liquid clouds
during the campaign. This model bias is traced back to the way cloud condensation nuclei (CCN) get activated into cloud droplets in the two-moment
bulk microphysical scheme used in this study. This process is parameterized as a function of grid-scale vertical velocity in the microphysical scheme
used, but in-cloud turbulence cannot be sufficiently resolved at 1.2 km horizontal resolution in Arctic clouds. By parameterizing
subgrid-scale vertical motion as a function of turbulent kinetic energy, we are able to achieve a more realistic CCN activation into cloud
droplets. Additionally, we showed that by scaling the presently used CCN activation profile, the hydrometeor number concentration could be modified
to be in better agreement with ACLOUD observations in our revised CCN activation parameterization. This consequently results in an improved
representation of cloud optical properties in our ICON simulations.
Reactive Organic Carbon Emissions from Volatile Chemical Products
Authors: Seltzer Karl M. , Pennington Elyse , Rao Venkatesh , Murphy Benjamin N. , Strum Madeleine , Isaacs Kristin K. , Pye Havala O. T.
Published: November 2020

Abstract. Volatile chemical products (VCPs) are an increasingly important source of anthropogenic reactive organic carbon (ROC) emissions. Among these sources are everyday items, such as personal care products, general cleaners, architectural coatings, pesticides, adhesives, and printing inks. Here, we develop VCPy, a new framework to model organic emissions from VCPs throughout the United States, including spatial allocation to regional and local scales. Evaporation of species in the VCPy framework is a function of the compound specific physiochemical properties that govern volatilization and the timescale relevant for product evaporation. We introduce the terms evaporation timescale and use timescale, respectively, to describe these processes. Using this framework, predicted national, per-capita organic emissions from VCPs are 9.7 kg person−1 year−1 (6.5 kgC person−1 year−1) for 2016, which translates to 3.12 Tg (2.10 TgC), making VCPs a dominant source of anthropogenic organic emissions in the United States. Uncertainty associated with this framework and sensitivity to select parameters were characterized through Monte Carlo analysis, resulting in a 95 % confidence interval of national VCP emissions for 2016 of 2.68–3.60 Tg (1.81–2.42 TgC). This nationwide total is broadly consistent with the US EPA's 2017 National Emission Inventory (NEI); however, county-level and categorical estimates can differ substantially from NEI values. VCPy predicts larger VCP emissions than the NEI for approximately half of all counties, with 5 % of all counties featuring increases > 60%. Categorically, personal care products (150 %) and paints/coatings (34 %) feature the largest increases, whereas pesticides (−54 %) and printing inks (−13 %) feature the largest decreases. An observational evaluation indicates emissions of key species from VCPs are reproduced with high fidelity in the methods employed here (normalized mean bias of −13 % with r = 0.95). Sector-wide, the effective secondary organic aerosol yield and maximum incremental reactivity of VCPs are 5.3 % by mass and 1.59 g O3 g−1, respectively, indicating VCPs are an important, and likely underrepresented to-date, source of secondary pollution in urban environments.
In-depth characterization of submicron particulate matter inter-annual variations at a street canyon site in Northern Europe
Authors: Barreira Luis M. F. , Helin Aku , Aurela Minna , Teinilä Kimmo , Friman Milla , Kangas Leena , Niemi Jarkko V. , Portin Harri , Kousa Anu , Pirjola Liisa , Rönkkö Topi , Saarikoski Sanna , Timonen Hilkka
Published: November 2020

Abstract. Atmospheric aerosols play an important role in air pollution. Aerosol particle's chemical composition is highly variable depending on the season, hour of the day, day of the week, meteorology, and the location of the measurement site. Long measurement periods and high time-resolved data are required in order to achieve statistically relevant amount of data for assessing those variations and evaluate pollution episodes. In this study, we present continuous atmospheric PM1 (particulate matter
Improving GOES Advanced Baseline Imager (ABI) aerosol optical depth (AOD) retrievals using an empirical bias correction algorithm
Authors: Zhang Hai , Kondragunta Shobha , Laszlo Istvan , Zhou Mi
Published: November 2020

Abstract. The Advanced Baseline Imager (ABI) on board the
Geostationary Operational Environmental Satellite-R (GOES-R) series enables
retrieval of aerosol optical depth (AOD) from geostationary satellites using
a multiband algorithm similar to those of polar-orbiting satellites'
sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS)
and Visible Infrared Imaging Radiometer Suite (VIIRS). However, this work
demonstrates that the current version of GOES-16 (GOES-East) ABI AOD has
diurnally varying biases due to limitations in the land surface reflectance
relationships between the 0.47 µm band and the 2.2 µm band and between the 0.64 µm band and 2.2 µm band used in the ABI AOD
retrieval algorithm, which vary with the Sun–satellite geometry and NDVI
(normalized difference vegetation index). To reduce these biases, an
empirical bias correction algorithm has been developed based on the lowest
observed ABI AOD of an adjacent 30 d period and the background AOD at each
time step and at each pixel. The bias correction algorithm improves the
performance of ABI AOD compared to AErosol RObotic NETwork (AERONET) AOD,
especially for the high and medium (top 2) quality ABI AOD. AOD data for the period 6 August to 31 December 2018 are used to evaluate the bias
correction algorithm. After bias correction, the correlation between the top 2 quality ABI AOD and AERONET AOD improves from 0.87 to 0.91, the mean bias
improves from 0.04 to 0.00, and root-mean-square error (RMSE) improves from
0.09 to 0.05. These results for the bias-corrected top 2 qualities ABI AOD
are comparable to those of the corrected high-quality ABI AOD. By using the
top 2 qualities of ABI AOD in conjunction with the bias correction
algorithm, the areal coverage of ABI AOD is increased by about 100 %
without loss of data accuracy.
Highly time-resolved characterization of carbonaceous aerosols using a two-wavelength Sunset thermo/optical carbon analyzer
Authors: Bao Mengying , Zhang Yan-Lin , Cao Fang , Lin Yu-Chi , Wang Yuhang , Liu Xiaoyan , Zhang Wenqi , Fan Meiyi , Xie Feng , Cary Robert , Dixon Joshua , Zhou Lihua
Published: November 2020

Abstract. Carbonaceous aerosols have great influence on the air quality, human health and climate change. Except for organic carbon (OC) and elemental carbon (EC), brown carbon (BrC), mainly originates from biomass burning, as a group of OC with strong absorption from the visible to near-ultraviolet wavelengths, makes a considerable contribution to global warming. Large amounts of studies have reported long-term observation of OC and EC concentrations throughout the word, but studies of BrC based on long-term observations are rather limited. In this study, we established a two-wavelength method (658 nm and 405 nm) applied in the Sunset thermo/optical carbon analyzer. Based on one-year observation, we firstly investigated the characteristics, meteorological impact and transport process of OC and EC. Due to BrC absorbs light at 405 nm more effectively than 658 nm, we defined the enhanced concentrations (dEC = EC405 nm−EC658 nm) and gave the possibility to provide an indicator of BrC. The receptor model and MODIS fire information were used to identify the presence of BrC aerosols. Our results showed that the carbonaceous aerosols concentrations were highest in winter and lowest in summer. Traffic emission was an important source of carbonaceous aerosols in Nanjing. Receptor model results showed that strong local emissions were found in OC and EC aerosols, however dEC aerosols were significantly affected by regional or long-range transport. The dEC / OC and OC / EC ratios showed similar diurnal patterns and the dEC / OC increased when the OC / EC ratios increased, indicating strong secondary sources or biomass burning contributions to dEC. Two biomass burning events both in summer and winter were analyzed and the results showed that the dEC concentrations were obvious higher in biomass burning days, however, no similar levels of the OC and EC concentrations were found both in biomass burning days and normal days in summer, suggesting that biomass burning emission made a great contribution to dEC and the sources of OC and EC were more complicated. Large number of open fire counts from the northwest and southwest areas of the study site were monitored in winter, significantly contributed to OC, EC and dEC. In addition, the near-by YRD area was one of the main potential source areas of dEC, suggesting that anthropogenic emissions could also be important sources of dEC. The results proved that dEC can be an indicator of BrC in biomass burning days. Our modified two-wavelength instrument provided more information than traditional single-wavelength thermo/optical carbon analyzer and gave a new idea about the measurement of BrC, the application of dEC data need to be further investigated.
A continued role of short-lived climate forcers under the Shared Socioeconomic Pathways
Authors: Lund Marianne T. , Aamaas Borgar , Stjern Camilla W. , Klimont Zbigniew , Berntsen Terje K. , Samset Bjørn H.
Published: November 2020

Abstract. Mitigation of non-CO2 emissions plays a key role in meeting the Paris
Agreement ambitions and sustainable development goals. Implementation of
respective policies addressing these targets mainly occur at sectoral and
regional levels, and designing efficient mitigation strategies therefore
relies on detailed knowledge about the mix of emissions from individual
sources and their subsequent climate impact. Here we present a comprehensive
dataset of near- and long-term global temperature responses to emissions of
CO2 and individual short-lived climate forcers (SLCFs) from 7 sectors
and 13 regions – for both present-day emissions and their continued evolution as
projected under the Shared Socioeconomic Pathways (SSPs). We demonstrate the key
role of CO2 in driving both near- and long-term warming and
highlight the importance of mitigating methane emissions from agriculture,
waste management, and energy production as the primary strategy to further
limit near-term warming. Due to high current emissions of cooling SLCFs,
policies targeting end-of-pipe energy sector emissions may result in net
added warming unless accompanied by simultaneous methane and/or CO2
reductions. We find that SLCFs are projected to play a continued role in
many regions, particularly those including low- to medium-income countries,
under most of the SSPs considered here. East Asia, North America, and Europe will
remain the largest contributors to total net warming until 2100, regardless
of scenario, while South Asia and Africa south of the Sahara overtake Europe
by the end of the century in SSP3-7.0 and SSP5-8.5. Our dataset is made
available in an accessible format, aimed also at decision makers, to
support further assessment of the implications of policy implementation at
the sectoral and regional scales.
A computationally efficient method for probabilistic local warming projections constrained by history matching and pattern scaling, demonstrated by WASP–LGRTC-1.0
Authors: Goodwin Philip , Leduc Martin , Partanen Antti-Ilari , Matthews H. Damon , Rogers Alex
Published: November 2020

Abstract. Climate projections are made using a hierarchy of models of different
complexities and computational efficiencies. While the most complex
climate models contain the most detailed representations of many
physical processes within the climate system, both parameter space
exploration and integrated assessment modelling require the increased
computational efficiency of reduced-complexity models. This study
presents a computationally efficient method for generating
probabilistic projections of local warming across the globe, using a
pattern-scaling approach derived from the Climate Model
Intercomparison Project phase 5 (CMIP5) ensemble, that can be coupled
to any efficient model ensemble simulation of global mean surface
warming. While the method can project local warming for arbitrary
future scenarios, using it for scenarios with peak global mean warming
≤2 ∘C is problematic due to the large
uncertainties involved. First, global mean warming is projected using
a 103-member ensemble of history-matched simulations with an
example reduced complexity Earth system model: the Warming
Acidification and Sea-level Projector (WASP). The ensemble projection
of global mean warming from this WASP ensemble is then converted into
local warming projections using a pattern-scaling analysis from the
CMIP5 archive, considering both the mean and uncertainty of the local
to global ratio of temperature change (LGRTC) spatial patterns from
the CMIP5 ensemble for high-end and mitigated scenarios. The LGRTC
spatial pattern is assessed for scenario dependence in the CMIP5
ensemble using RCP2.6, RCP4.5 and RCP8.5, and spatial domains are
identified where the pattern scaling is useful across a variety of
arbitrary scenarios. The computational efficiency of our WASP–LGRTC
model approach makes it ideal for future incorporation into an
integrated assessment model framework or efficient assessment of
multiple scenarios. We utilise an emergent relationship between
warming and future cumulative carbon emitted in our simulations to
present an approximation tool making local warming projections from
total future carbon emitted.
Sensitivity of Northern Hemisphere climate to ice-ocean interface
heat flux parameterizations
Authors: Shi Xiaoxu , Notz Dirk , Liu Jiping , Yang Hu , Lohmann Gerrit
Published: November 2020

Abstract. We investigate the impact of three different parameterizations of ice-ocean heat exchange on modeled ice thickness, ice concentration, and water masses. These three parameterizations are (1) an ice-bath assumption with the ocean temperature fixed at the freezing temperature, (2) a turbulent heat-flux parameterization with ice-ocean heat exchange depending linearly on the temperature difference between the mixed layer and the ice-ocean interface, and (3) a similar turbulent heat-flux parameterization as (2) but with the temperature at the ice-ocean interface depending on ice-ablation rate. Based on model simulations with the standalone sea-ice model CICE, the ice-ocean model MPIOM and the climate model COSMOS, we find that (3) leads (in comparison to the other two parameterizations) to a thicker modeled sea ice, warmer water beneath high-concentration ice and cooler water towards the ice edge, and higher salinity in the Arctic Ocean mixed layer. Finally, in the fully coupled climate model COSMOS, the most realistic parameterization leads to an enhanced Atlantic meridional overturning circulation (AMOC), a more positive North Atlantic Oscillation (NAO) mode and a weakened Aleutian Low.
Hysteresis in soil hydraulic conductivity as driven by salinity
and sodicity: a modeling framework
Authors: Kramer Isaac , Bayer Yuval , Adeyemo Taiwo , Mau Yair
Published: November 2020

Abstract. Declines in soil saturated hydraulic conductivity (Ks) as a result of saline and sodic irrigation water are a major cause of soil degradation. While it is understood that the mechanisms that lead to degradation can cause irreversible changes in Ks, existing models do not account for hysteresis between the degradation and rehabilitation processes. We develop the first model for the effect of saline and sodic water on Ks that explicitly includes hysteresis. As such, the idea that a soil's history of degradation and rehabilitation determines its future Ks lies at the center of our model. By means of a weight function, the model accounts for soil specific differences, such as clay content. The weight function also determines the form of the hysteresis curves, which are not restricted to a single shape, as in some existing models for irreversible soil processes. The concept of the weight function is used to develop a reversibility index, which allows for the quantitative comparison of different soils and their susceptibility to irreversible degradation. We discuss the experimental setup required to find a soil's weight function and show how the weight function determines the degree to which Ks is reversible, for a given soil. We demonstrate the feasibility of this procedure by presenting novel experimental results showcasing the presence of hysteresis in soil Ks, and using these results to calculate a weight function. Past experiments and models on the decline of Ks due to salinity and sodicity focus on degradation alone, ignoring any characterization of the degree to which declines in Ks are reversible. Our model and experimental results emphasize the need to measure reversal curves, obtained from rehabilitation measurements following mild declines in Ks. The developed model has the potential to significantly improve our ability to assess the risk of soil degradation, by allowing for the consideration of how the accumulation of small degradation events can cause significant land degradation.
A multidisciplinary drought catalogue for southwestern Germany dating back to 1801
Authors: Erfurt Mathilde , Skiadaresis Georgios , Tijdeman Erik , Blauhut Veit , Bauhus Jürgen , Glaser Rüdiger , Schwarz Julia , Tegel Willy , Stahl Kerstin
Published: November 2020

Abstract. Droughts are multidimensional hazards that can lead to substantial
environmental and societal impacts. To understand causes and impacts,
multiple perspectives need to be considered. Many studies have identified
past drought events and investigated drought propagation from meteorological
droughts via soil moisture to hydrological droughts, and some studies have
included the impacts of these different types of drought. However, it is not
certain whether the increased frequency and severity of drought events in
the past decade is unprecedented in recent history. Therefore, we analyze
different droughts and their impacts in a regional context using a
multidisciplinary approach. We compile a comprehensive and long-term dataset to investigate possible temporal patterns in drought occurrence and
place recent drought events into a historical context. We assembled a
dataset of drought indices and recorded impacts over the last 218 years in
southwestern Germany. Meteorological and river-flow indices were used to
assess the natural drought dynamics. In addition, tree-ring data and
recorded impacts were utilized to investigate drought events from an
ecological and social perspective. Since 1801, 20 extreme droughts have been identified as common extreme events when applying the different indices. All
events were associated with societal impacts. Our multi-dataset approach
provides insights into similarities but also the unique aspects of different
drought indices.
Influence of intraseasonal eastern boundary circulation variability on hydrography and biogeochemistry off Peru
Authors: Lüdke Jan , Dengler Marcus , Sommer Stefan , Clemens David , Thomsen Sören , Krahmann Gerd , Dale Andrew W. , Achterberg Eric P. , Visbeck Martin
Published: November 2020

Abstract. The intraseasonal evolution of physical and biogeochemical properties during
a coastal trapped wave event off central Peru is analysed using data from an
extensive shipboard observational programme conducted between April and
June 2017, and remote sensing data. The poleward velocities in the Peru–Chile
Undercurrent were highly variable and strongly intensified to above
0.5 m s−1 between the middle and end of May. This intensification was likely
caused by a first-baroclinic-mode downwelling coastal trapped wave, excited
by a westerly wind anomaly at the Equator and originating at about
95∘ W. Local winds along the South American coast did not impact
the wave. Although there is general agreement between the observed
cross-shore-depth velocity structure of the coastal trapped wave and the
velocity structure of first vertical mode solution of a linear wave model,
there are differences in the details of the two flow distributions. The
enhanced poleward flow increased water mass advection from the equatorial
current system to the study site. The resulting shorter alongshore transit
times between the Equator and the coast off central Peru led to a strong
increase in nitrate concentrations, less anoxic water, likely less fixed
nitrogen loss to N2 and a decrease of the nitrogen deficit compared to
the situation before the poleward flow intensification. This study
highlights the role of changes in the alongshore advection due to coastal
trapped waves for the nutrient budget and the cumulative strength of
N cycling in the Peruvian oxygen minimum zone. Enhanced availability of
nitrate may impact a range of pelagic and benthic elemental cycles, as it
represents a major electron acceptor for organic carbon degradation during
denitrification and is involved in sulfide oxidation in sediments.
Improved calibration of Green-Ampt infiltration in the
EROSION-2D/3D model using a rainfall-runoff experiment
database
Authors: Beitlerová Hana , Lenz Jonas , Devátý Jan , Mistr Martin , Kapička Jiří , Buchholz Arno , Gerndtová Ilona , Routschek Anne
Published: November 2020

Abstract. Soil infiltration is one of the key factors that has an influence on soil erosion caused by rainfall. Therefore, a well-represented infiltration process is a necessary precondition for successful soil erosion modelling. Complex natural conditions do not allow the full mathematical description of the infiltration process and additional calibration parameters are required. The Green-Ampt based infiltration module in the EROSION-2D/3D model is adjusted by calibration of the skinfactor parameter. Previous studies provide skinfactor values for several combinations of soil and vegetation conditions. However, their accuracies are questionable and estimating the skinfactors for other than the measured conditions yields significant uncertainties in the model results. This study presents new empirically based transfer functions for skinfactor estimation that significantly improve the accuracy of the infiltration module and thus the overall EROSION-2D/3D model performance. The transfer functions are based on a statistical analysis of the rainfall-runoff simulation database, which contains 273 experiments compiled by two independent working groups. Linear mixed effects models, with a manual backward elimination approach for the predictor selection, were applied to derive the transfer functions. Soil moisture and bulk density were identified as the most significant predictors explaining 79 % of the skinfactor variability, followed by the soil texture and the impact of previous rainfall events. The mean absolute percentage error of the skinfactor prediction was improved from 192 % using the currently available method, to 66 % using the presented transfer functions. Error propagation of the predicted skinfactors into the surface runoff and soil loss on the hypothetical slope showed significant improvement in the EROSION-2D/3D results. A first validation of real rainfall-runoff events indicates good model performance for events with a higher total precipitation and intensity.
SoilGrids 2.0: producing quality-assessed soil information for the
globe
Authors: de Sousa Luis M. , Poggio Laura , Batjes Niels H. , Heuvelink Gerard B. M. , Kempen Bas , Riberio Eloi , Rossiter David
Published: November 2020

Abstract. SoilGrids produces maps of soil properties for the entire globe at medium spatial resolution (250 metres cell size) using state-of-the-art machine learning methods to generate the necessary models. It takes as inputs soil observations from about 240 000 locations worldwide and over 400 global environmental covariates describing vegetation, terrain morphology, climate, geology and hydrology. The aim of this work was the production of quality-assessed global maps of soil properties, with cross-validation, hyper-parameters selection and quantification of spatially explicit uncertainty, as implemented in the SoilGrids version 2.0 product incorporating state of the art practices and adapting them for global digital soil mapping with legacy data. The paper presents the evaluation of the global predictions produced for soil organic carbon content, total nitrogen, coarse fragments, pH(water), cation exchange capacity, bulk density and texture fractions at six standard depths (up to 200 cm). The quantitative evaluation showed metrics in line with previous global, continental and large regions studies. The qualitative evaluation showed that coarse scale patterns are well reproduced. The spatial uncertainty at global scale highlighted the need for more soil observations, especially in high latitude regions.
Safety and effectiveness of adalimumab in the treatment of ulcerative colitis: results from a large-scale, prospective, multicenter, observational study
Authors: Ogata Haruhiko , Hagiwara Takashi , Kawaberi Takeshi , Kobayashi Mariko , Hibi Toshifumi
Published: November 2020

Background/Aims: Adalimumab has been shown to induce and maintain clinical remission in patients with moderate to severe ulcerative colitis (UC). However, no large-scale population-based studies have been performed in Japan. This study was conducted to evaluate the safety and effectiveness of adalimumab in clinical practice in Japanese patients with UC.Methods: In this 52-week, prospective, multicenter, single-cohort, noninterventional, observational, postmarketing surveillance study, patients with moderate to severe UC received an initial subcutaneous injection of adalimumab 160 mg, followed by 80 mg at 2 weeks, and then 40 mg every other week. Safety assessments were the incidence of adverse drug reactions (ADRs) and serious ADRs. Effectiveness assessments were clinical remission, corticosteroid-free remission, mucosal healing, and change in C-reactive protein (CRP) levels from baseline.Results: Of 1,593 registered patients, 1,523 (male, 57.6%; mean age, 41.8 years) and 1,241 patients were included in the safety and effectiveness populations, respectively. ADRs were reported in 18.1% and serious ADRs in 4.9% of patients. Clinical remission was achieved in 49.7% of patients at week 4, increasing to 74.4% at week 52. Corticosteroid-free remission rates increased over time, from 10.4% at week 4 to 53.1% at week 52. More than 60% of patients demonstrated mucosal healing at weeks 24 and 52. Mean CRP levels (mg/dL) decreased from 1.2 at baseline to 0.6 at week 4 and 0.3 at week 52.Conclusions: This large real-world study confirmed the safety and effectiveness of adalimumab in patients with UC in Japan. No new safety concerns were identified.
Human Resources for Tourism and Travel Business in Vietnam
Authors: Hien Tran Thi , Huu Cuong Hoa , Thi Phuong Loan Nguyen , Thi Nang Do , Hoang Anh Le , Quang Sang Nguyen
Published: November 2020

Vietnam is facing a shortage in both the quantity and quality of human resources for tourism and travel business. This shortage becomes even more urgent after the ASEAN agreement on the free movement of tourism human resources among Southeast Asian countries. With the trend of extensive international economic integration, increasing digital technology, tourism business activities have also changed. According to the trend of “smart tourism”, tourism business enterprises require that human resources must ensure skills, profession and proficiency in using smart tourism management software and technologies. Therefore, tourism training institutions need to quickly approach and apply advanced applications of intelligent technology, artificial intelligence in teaching and practicing. The paper focuses on analyzing the current situation of human resources in tourism and travel industry in Vietnam, the training and retraining of this workforce in the past time. From there, propose some solutions to improve the training and retraining of human resources for tourism and travel business in the future.
Implicações das tecnologias híbridas de fabricação nos processos de reparo e remanufatura de peças no contexto da manutenção: uma revisão sistemática
Authors: Velázquez Daniel René Tasé , Helleno André Luís , Lima Carlos Roberto Camello , Mastrapa Lorena Hernández
Published: November 2020

A flexibilidade das tecnologias híbridas de fabricação (HMT) para integrar os processos produtivos e as atividades de manutenção contribui para o aumento do ciclo de vida de peças funcionais ou remanufaturadas, agregando-lhes valor, dado que, pelo elevado custo de fabricação, não se justifica seu descarte. O objetivo deste artigo é investigar quais são os benefícios do uso das HMT nas atividades de reparo e remanufatura de peças no contexto da manutenção. A revisão sistemática da literatura relacionada ao tema mostrou que o emprego das HMT para executar atividades de reparo e remanufatura de peças como parte dos sistemas de manutenção está em expansão. Além disso, foi identificado que as aplicações das HMT visam o aumento do ciclo de vida dos produtos, a eficiência de recursos e remanufatura de peças no fim da vida útil como parte da reconfiguração da cadeia de valor.
Ferramenta computacional para análise do comportamento da população do algoritmo genético no espaço de soluções
Authors: Araújo Sidnei Alves de , Lima Stanley Jefferson Araujo
Published: November 2020

O Algoritmo Genético (AG) é um método otimização inspirado na teoria de evolução das espécies que tem sido largamente empregado na solução de problemas classificados como NP-Hard (Non-deterministic Polynomial Time), entre os quais estão o Problema de Sequenciamento da Produção (Job Shop Scheduling Problem - JSP) e o Problema de Roteamento de Veículos (PRV). Entretanto, encontrar soluções para qualquer problema de otimização empregando o AG pressupõe a adoção de um esquema de codificação das soluções e a configuração dos operadores genéticos. Diferentes esquemas e configurações podem produzir comportamentos diferentes na população do AG, mas observar tais comportamentos não é uma tarefa fácil e, por este motivo, vem atraindo a atenção de muitos pesquisadores ao longo dos últimos anos. Neste trabalho propõe-se uma ferramenta computacional que permite analisar como o esquema de codificação e os operadores genéticos afetam o comportamento da população do AG no espaço de soluções, por meio de visualização dos indivíduos projetados para um espaço bidimensional e de medidas de desempenho implementadas na ferramenta. Nos experimentos conduzidos analisou-se o comportamento da população do AG em função de três esquemas de codificação de soluções para o Problema de Roteamento de Veículos Capacitados (PRVC). Como resultados, além de uma discussão acerca da análise do comportamento do AG, pode-se constatar que as medidas de desempenho fornecidas pela ferramenta computacional desenvolvida podem auxiliar na proposição e/ou escolha de heurísticas que visem apoiar o processo de refinamento das soluções geradas pelo AG, melhorando o seu desempenho.
Estimation of co-variance components, genetic parameters, and genetic trends of reproductive traits in community-based breeding program of Bonga sheep in Ethiopia
Authors: Areb Ebadu , Getachew Tesfaye , Kirmani MA , G.silase Tegbaru , Haile Aynalem
Published: November 2020

Objective: The objectives of the study were to evaluate reproductive performance and selection response through genetic trend of community-based breeding programs (CBBPs) of Bonga sheep.Methods: Reproduction traits data were collected between 2012 and 2018 from Bonga sheep CBBPs. Phenotypic performance was analyzed using the general linear model procedures of Statistical Analysis System. Genetic parameters were estimated by univariate animal model for age at first lambing (AFL) and repeatability models for lambing interval (LI), litter size (LS), and annual reproductive rate (ARR) traits using restricted maximum likelihood method of WOMBAT. For correlations bivariate animal model was used. Best model was chosen based on likelihood ratio test. The genetic trends were estimated by the weighted regression of the average breeding value of the animals on the year of birth/ lambing.Results: The overall least squares mean±standard error of AFL, LI, LS, and ARR were 375± 12.5, 284±9.9, 1.45±0.010, and 2.31±0.050, respectively. Direct heritability estimates for AFL, LI, LS, and ARR were 0.07±0.190, 0.06±0.120, 0.18±0.070, and 0.25±0.203, respectively. The low heritability for both AFL and LI showed that these traits respond little to selection programs but rather highly depend on animal management options. The annual genetic gains were –0.0281 days, –0.016 days, –0.0002 lambs and 0.0003 lambs for AFL, LI, LS, and ARR, respectively.Conclusion: Implications of the result to future improvement programs were improving management of animals, conservation of prolific flocks and out scaling the CBBP to get better results.
Effect of myoglobin, hemin, and ferric iron on quality of chicken breast meat
Authors: Zhang Muhan , Yan Weili , Wang Daoying , Xu Weimin
Published: November 2020

Objective: The objective was to evaluate the impact of different forms of iron including myoglobin, hemin, and ferric chloride on the quality of chicken breast meat. Methods: Chicken breast muscles were subjected to 1, 2, 3 mg/mL of FeCl3, myoglobin and hemin treatment respectively, and the production of reactive oxygen species (ROS) and malondialdehyde, meat color, tenderness, water holding capacity and morphology of meat was evaluated. Results: Hemin was found to produce more ROS and induce greater extent of lipid oxidation than myoglobin and ferric chloride. However, it showed that hemin could significantly increase the redness and decrease the lightness of the muscle. Hemin was also shown to be prominent in improving water holding capacity of meat, maintaining a relatively higher level of the immobilized water from low-field nuclear magnetic resonance measurements. Morphology observation by hematoxylin-eosin staining further confirmed the results that hemin preserved the integrity of the muscle. Conclusion: The results indicated that hemin may have economic benefit for the industry based on its advantage in improving water holding capacity and quality of meat.
Hate Speech Detection in Portuguese with Naïve Bayes, SVM, MLP and Logistic Regression
Authors: Silva Adriano , Roman Norton
Published: November 2020

Even though social networks can provide free space for discussing ideas, people can also use them to propagate hate speech and, given the amount of written material in such networks, it becomes necessary to rely on automatic methods for identifying this problem. In this work, we set out to verify the use of some classic Machine Learning algorithms for the task of hate speech detection in tweets written in Portuguese, by testing four different models (SVM, MLP, Logistic Regression and Naïve Bayes) with different configurations. Results show that these algorithms produce better results (in terms of micro-averaged F1 score) than the LSTM used for benchmark, being also competitive to other results by the related literature
Avaliação empírica de classificadores e métodos de balanceamento para detecção de fraudes em transações com cartões de créditos
Authors: Nicola Victor , Lauretto Marcelo , Valdivia Delgado Karina
Published: November 2020

Algoritmos de aprendizado de máquina são amplamente utilizados em sistemas para detecção de fraudes em cartões de crédito devido à capacidade de distinguir entre transações legítimas e fraudulentas. Um problema reconhecido nesta área é o alto desbalanceamento usualmente encontrado nas classes, que pode comprometer o desempenho dos classificadores. Os estudos empíricos encontrados na literatura aplicam, no máximo, duas técnicas de amostragem. Este artigo traz um estudo comparativo de cinco modelos de classificação sob cinco diferentes métodos de balanceamento dos conjuntos de treinamento. O melhor desempenho foi obtido pela random forest, que além de ter apresentado o maior F-score médio (0,867), mostrou-se consideravelmente mais robusta do que os demais classificadores em relação à escolha da técnica de balanceamento e à seleção de atributos.
Algoritmo Híbrido para o Problema Flow Shop de Permutação Multiobjetivo
Authors: Fiorini Júnior Volmir , Almeida Carolina , Venske Sandra
Published: November 2020

Os algoritmos evolucionários são uma abordagem não determinística para resolver problemas de otimização que não podem ser resolvidos em tempo polinomial, como problemas clássicos NP-Hard. O Flow Shop de Permutação (FSP) é um problema de otimização combinatória do ambiente de produção, em que tarefas devem ser processadas por máquinas, mantendo o mesmo fluxo de processamento. Neste trabalho a abordagem multiobjetivo foi utilizada para o FSP, tendo como objetivos de minimização o makespan e o total flowtime. Dois algoritmos híbridos compostos por NSGA-II com Busca Tabu foram considerados na abordagem e aplicados em 11 instâncias do FSP com diferentes dimensões. Uma análise foi feita sobre o uso de regras de proibição na Busca Tabu e sua restritividade. Os resultados foram analisados utilizando as métricas de qualidade IGD e Função de Conquista Empírica, comparando-os com o NSGA-II canônico.
Inferência Automática do Nível de Dificuldade em Receitas Culinárias usando Técnicas de Processamento de Linguagem Natural
Authors: Britto Larissa , Pacífico Luciano , Ludermir Teresa
Published: November 2020

Neste trabalho, será proposta uma ferramenta de inferência do nível de dificuldade de receitas culinárias. A inferência será feita através da classificação textual dos modos de preparo das receita. A ferramenta será parte fundamental no desenvolvimento de um sistema de recomendação de receitas culinárias sensível ao contexto baseado em conhecimento. Serão adotados alguns dos principais classificadores da literatura de Classificação de Texto, além de diferentes métodos de extração de características. Uma avaliação experimental é executada, no intuito de selecionar as melhores abordagens para compor o sistema.
CbDGen: Uma ferramenta de geração de dados sintéticos baseada na complexidade
Authors: França Thiago , Miranda Péricles , Prudêncio Ricardo , Nascimento André
Published: November 2020

Ferramentas de geração de bases de dados sintéticos ganharam atenção como uma alternativa para a avaliação eficaz de classificadores. Como são altamente configuráveis, as ferramentas possibilitam a criação de bases com características específicas, permitindo a avaliação do classificador em diferentes cenários. Este trabalho propõe a CbDGen, uma ferramenta para geração de bases sintéticas baseada em complexidade de dados. Com uma interface simples e parametrizável, a CbDGen possibilita a inserção de informações básicas (i.e., número de classes, atributos), a seleção de diferentes distribuições e nível de ruído. O seu grande diferencial é permitir que a escolha do tipo de complexidade desejado (i.e., balanceamento, separabilidade), bem como o nível desta complexidade. Os resultados mostraram que a CbDGen é capaz de produzir bases sintéticas aderentes às escolhas realizadas pelo usuário, se mostrando uma plataforma propícia para a avaliação de classificadores.
Uma Abordagem Híbrida para Detecção de Moedas de Real Baseada em Algoritmos de Localização e Redes Neurais Convolucionais
Authors: Yonekura David , Guedes Elloá
Published: November 2020

A detecção de múltiplas moedas é um problema de Visão Computacional que objetiva encontrar moedas em uma imagem e classificá-las quanto aos seus respectivos valores. Para o Real, a literatura tipicamente considera o uso de técnicas de extração de características seguida de classificadores de Machine Learning, em especial as redes neurais artificiais. Porém, considerando resultados recentes com Deep Learning, houve a motivação de propor uma abordagem híbrida considerando a localização de moedas com técnicas tradicionais de Visão Computacional e classificação com Redes Neurais Convolucionais. Múltiplas arquiteturas foram avaliadas e a solução proposta possui mean average precision superior a 92% com acurácia de classificação maior que 97%, superando outro resultado em 6%.
A Network-Based High-Level Data Classification Algorithm Using Betweenness Centrality
Authors: Zuñiga Esteban , Zhao Liang
Published: November 2020

Data classification is a major machine learning paradigm, which has been widely applied to solve a large number of real-world problems. Traditional data classification techniques consider only physical features (e.g., distance, similarity, or distribution) of the input data. For this reason, those are called low-level classification. On the other hand, the human (animal) brain performs both low and high orders of learning, and it has a facility in identifying pat-terns according to the semantic meaning of the input data. Data classification that considers not only physical attributes but also the pattern formation is referred to as high-level classification. Several high-level classification techniques have been developed, which make use of complex networks to characterize data patterns and have obtained promising results. In this paper, we propose a pure network-based high-level classification technique that uses the betweenness centrality measure. We test this model in nine different real datasets and compare it with other nine traditional and well-known classification models. The results show us a competent classification performance.
Sample Bias Effect on Meta-Learning
Authors: Reis Mariane , Lorena Ana
Published: November 2020

Sample bias is a common issue on traditional machine learning studies but rarely considered when discussing meta-learning. It happens when the training data sample lacks or overemphasizes one or more characteristics, compared to others. Herewith, models trained on such data may become inaccurate for some instances. This work aims to analyze this issue in the meta-learning context. Indeed, in most of the meta-learning literature, a random sample of datasets is taken for building meta-models. Nonetheless, there is no discussion over a possible side-effect bias not controlled in such random sampling. This work aims to analyze these effects, in order not only to discuss their consequences, but also to start a debate over the need of their consideration in meta-learning research.
On the Analysis of Mutation Operators in Multiobjective Cartesian Genetic Programming for Designing Combinational Logic Circuits
Authors: Souza Lucas , Bernardino Heder
Published: November 2020

Approximate Computing is an emerging paradigm that takes advantage of inherently error resilient digital circuits to design circuits with higher energetic efficiency, lower delay, or a smaller occupied area on the chips. Traditional approaches do not handle multiple objectives and metaheuristics appear as a proper alternative. In particular, Multiobjective Cartesian Genetic Programming (MOCGP) can find good solutions to the design and optimization of approximate circuits. The performance of CGP depends on the mutation adopted, as normally CGP only uses mutation for creating new solutions. However, to the best of our knowledge, just the traditional point mutation (PM) was used by the previously proposed MOCGP. Thus, the literature lacks an analysis of the best mutation operators of MOCGP. We propose here the analysis of PM and Single Active Mutation(SAM) on the multiobjective optimization of 15 heterogeneous combinational logic circuits from scratch and starting with a feasible solution. The results indicate that SAM obtained better results than PM.