bluuebunny/crossref_metadata_2025_split huggingface dataset:
Ionicity of Clay–Cation Bonds in Relation to Dispersive Behavior of Mg and K Soil Clays as Influenced by pH
Authors: Zhu Yingcan , Marchuk Alla , Bennett John McLean 
Published: November 2020

AbstractThe dispersive behavior dynamics of clay determine soil characteristics such as permeability and aggregate stability, and, consequently, crop productivity. Soil dispersion is heavily influenced by the ionicity of clay–cation bonds and has been shown to be related to the net negative charge and pH of the system. Little work has been done, however, which considers these factors together, especially for K and Mg clays. The objective of the present study was to investigate the effect of changing pH on the dispersive behavior of Mg and K homoionic clays, in comparison to Ca and Na clays under equivalent pH conditions. The clay fractions used here were extracted from three soils and have distinctly different mineralogies. These clays were treated to become homoionic with regard to Na, K, Ca, and Mg. Excess salts were removed by dialysis and pH was adjusted to 3, 4, 5, 6, 7, 8, 9, 10, and 11 for all clays, except Mg (pH range 3–7). Clay dispersion-flocculation dynamics were investigated, and the net negative charge, pH, electrical conductivity (EC), and turbidity were measured. Mg has a similar but less flocculative effect than Ca, while K has a similar but less dispersive effect than Na, under similar pH conditions. The dispersive behavior of Na, K, Mg, and Ca homoionic clays was correlated well with the ionicity of clay–cation bonds at equivalent pH, with the degree of clay dispersion being explained by the pH, EC, ionicity, ζ-potential, and mean particle size of the clay–cation system. A predictive model for dispersion was developed with its applicability and limitations discussed.
The Effect of Exposure to Sexual Violence on Sexual Dysfunction and Sexual Distress in Pregnant Women
Authors: Alan Dikmen Hacer , Çankaya Seyhan 
Published: November 2020

Abstract

Background
Sexual dysfunction and sexual distress are common during pregnancy, but the effects of exposure to sexual violence on sexual dysfunction and sexual distress in pregnant women is unknown.

Aim
The aim of this study was to determine the effects of sexual violence on female sexual dysfunction and sexual distress.

Methods
This is a descriptive study. Data were collected between December 2019 and April 2020 from 605 pregnant women.

Outcomes
The data were collected with a Data Collection Form (The socio-demographic and obstetric characteristics and exposure to sexual violence were evaluated with questions.), Female Sexual Function Index, and Female Sexual Distress Scale-Revised.

Results
In our study, sexual dysfunction was found in 72.9% of the pregnant women, and sexual distress was found in 38.2%. It was found that 11.6% of the women were exposed to sexual violence in their then-current pregnancy. While the rate of sexual distress was found to be significantly higher in women who were exposed to sexual violence during pregnancy (P &lt; .001), no significant difference was found between sexual violence and female sexual dysfunction (P = .572). Sexual violence during pregnancy was found to be a risk factor for sexual distress (P = .006).

Clinical Implications
This study may be an opportunity for nurses and physicians to determine the effects of sexual violence experienced by pregnant women on female sexual dysfunction and sexual distress during pregnancy.

Strength & Limitations
This is the first and only study that determines the effects of sexual violence on female sexual dysfunction and sexual distress. Our findings cannot be generalized to the entire population of Turkey.

Conclusion
The presence of female sexual dysfunction in women during pregnancy is quite high, and the rate of sexual distress is moderate. Sexual violence from partner/spouse during pregnancy is a risk factor for sexual distress.
Automatic classification of participant roles in cyberbullying: Can we detect victims, bullies, and bystanders in social media text?
Authors: Jacobs Gilles , Van Hee Cynthia , Hoste Véronique 
Published: November 2020

AbstractSuccessful prevention of cyberbullying depends on the adequate detection of harmful messages. Given the impossibility of human moderation on the Social Web, intelligent systems are required to identify clues of cyberbullying automatically. Much work on cyberbullying detection focuses on detecting abusive language without analyzing the severity of the event nor the participants involved. Automatic analysis of participant roles in cyberbullying traces enables targeted bullying prevention strategies. In this paper, we aim to automatically detect different participant roles involved in textual cyberbullying traces, including bullies, victims, and bystanders. We describe the construction of two cyberbullying corpora (a Dutch and English corpus) that were both manually annotated with bullying types and participant roles and we perform a series of multiclass classification experiments to determine the feasibility of text-based cyberbullying participant role detection. The representative datasets present a data imbalance problem for which we investigate feature filtering and data resampling as skew mitigation techniques. We investigate the performance of feature-engineered single and ensemble classifier setups as well as transformer-based pretrained language models (PLMs). Cross-validation experiments revealed promising results for the detection of cyberbullying roles using PLM fine-tuning techniques, with the best classifier for English (RoBERTa) yielding a macro-averaged${F_1}$-score of 55.84%, and the best one for Dutch (RobBERT) yielding an${F_1}$-score of 56.73%. Experiment replication data and source code are available athttps://osf.io/nb2r3.
Awareness and use of nutrition information predict measured and self-rated diet quality of older adults in the USA
Authors: Vaudin Anna , Wambogo Edwina , Moshfegh Alanna , Sahyoun Nadine R 
Published: November 2020

AbstractObjectives:To examine: (1) diet quality of older adults, using the Healthy Eating Index 2010 (HEI-2010) and self-rated diet quality, (2) characteristics associated with reported awareness and use of nutrition information and (3) factors associated with HEI score and self-rated diet quality.Design:Cross-sectional study. Based on Day 1 and/or Day 2 dietary recalls, the Per-Person method was used to estimate HEI-2010 component and total scores.T-tests and ANOVA were used to compare means. Logistic and linear regressions were used to test for associations with diet quality, controlling for potential confounders.Setting:National Health and Nutrition Examination Survey, 2009–2014.Participants:Three thousand and fifty-six adults, aged 60 years and older, who completed at least one 24-h recall and answered questions on awareness and use of nutrition information.Results:Mean HEI score for men was significantly lower than for women (56·4 ± 0·6v. 60·2 ± 0·6,P< 0·0001). Compared with men, more women were aware of (44·8 %v. 33·7 %,P< 0·05) and used (13·7 %v. 5·9 %,P< 0·05) nutrition information. In multivariable analyses, awareness and use of nutrition information were significant predictors of both HEI and self-rated diet quality for both women and men. Groups with lower nutrition awareness included men, non-Whites, participants in nutrition assistance programmes and those with lower education and socio-economic status.Conclusions:Nutrition awareness and use of nutrition information are associated with diet quality in adults 60 years and older. Gaps in awareness of dietary guidelines in certain segments of the older adult population suggest that targeted education may improve diet quality for these groups.
Compact stack EBG structure for enhanced isolation between stack patch antenna array elements for MIMO application
Authors: Kadu Mahesh B. , Rayavarapu Neela 
Published: November 2020

AbstractIn this research article, a compact wideband stack patch antenna array integrated with compact stack electromagnetic band gap (EBG) structure for multiple input multiple output (MIMO) application is proposed. The wide resonance bandwidth is achieved at 2.45 GHz band by stack arrangement of compact meander line slot driven and parasitic patch elements. The isolation bandwidth is matched with resonance bandwidth with the design of a compact stack L slot EBG structure. The polarization diversity and three-layer EBG structure ensure an enhancement in isolation level. To validate the performance of the proposed stack antenna array, a prototype was fabricated and tested for different MIMO parameters. The measured result confirms the effectiveness of the proposed antenna array in a diverse MIMO environment.
The Language of Being Alone and Being With Others
Authors: Uziel Liad 
Published: November 2020

Abstract. This article explores people’s perception of their experience while being alone or being with others. To that aim, data from nine studies (total N = 1,717) were meta-analyzed. In each study, participants performed a sentence-completion task referring to their experience alone/“with others”. The responses were analyzed using a text analysis software. Results showed that in a “with others” (vs. alone) mindset, people are more focused on the present, and less focused on the past or the future. When “with others,” more anxiety but less sadness is expressed. Social motives – affiliation and power – are more pronounced in the “with others” mindset, whereas leisure activities are salient in the alone mindset. Conclusions are derived regarding the nature of the experience in these basic conditions.
OI 630.0‐nm and N2 1PG Emissions in Pulsating Aurora Events Observed by an Optical Spectrograph at Tromsø, Norway
Authors: Tsuda T. T. , Li C. , Hamada S. , Hosokawa K. , Oyama S.‐i. , Nozawa S. , Kawabata T. , Mizuno A. , Kurihara J. , Nishiyama T. , Kosch M. J. 
Published: November 2020

AbstractWe performed observations of pulsating aurora (PsA) with an optical spectrograph at Tromsø, Norway, during wintertime in 2016–2017. The data analysis of multiple PsA events revealed the PsA spectra for the first time. As the results, the OI 630.0‐nm emissions and the N2 1PG emissions were found in the both spectra during brighter (ON) and darker (OFF) phases in the PsA events. The spectra of pulsations were derived as difference spectra between the ON and OFF spectra. From the obtained spectra of pulsations, it is found that dominant pulsations at 630.0 nm were coming from the N2 1PG (10,7) band, and there were less or minor contributions of the OI 630.0 nm to pulsations at 630.0 nm.
Seasonal Cycle of Isotope‐Based Source Apportionment of Elemental Carbon in Airborne Particulate Matter and Snow at Alert, Canada
Authors: Rodríguez B. T. , Huang L. , Santos G. M. , Zhang W. , Vetro V. , Xu X. , Kim S. , Czimczik C. I. 
Published: November 2020

AbstractElemental carbon (EC) is a major light‐absorbing component of atmospheric aerosol particles. Here, we report the seasonal variation in EC concentrations and sources in airborne particulate matter (PM) and snow at Alert, Canada, from March 2014 to June 2015. We isolated the EC fraction with the EnCan‐Total‐900 (ECT9) protocol and quantified its stable carbon isotope composition (δ13C) and radiocarbon content (∆14C) to apportion EC into contributions from fossil fuel combustion and biomass burning (wildfires and biofuel combustion). Ten‐day backward trajectories show EC aerosols reaching Alert by traveling over the Arctic Ocean from the Russian Arctic during winter and from North America (>40°N) during summer. EC concentrations range from 1.8–135.3 ng C m−3 air (1.9–41.2% of total carbon [TC], n = 48), with lowest values in summer (1.8–44.5 ng C m−3 air, n = 9). EC in PM (Δ14C = ‐532 ± 114‰ [ave. ± SD, n = 20]) and snow (−257 ± 131‰, n = 7) was depleted in 14C relative to current ambient CO2 year‐round. EC in PM mainly originated from liquid and solid fossil fuels from fall to spring (47–70% fossil), but had greater contributions from biomass burning in summer (48–80% modern carbon). EC in snow was mostly from biomass burning (53–88%). Our data show that biomass burning EC is preferentially incorporated into snow because of scavenging processes within the Arctic atmosphere or long‐range transport in storm systems. This work provides a comprehensive view of EC particles captured in the High Arctic through wet and dry deposition and demonstrates that surface stations monitoring EC in PM might underestimate biomass burning and transport.
Identification of Key Physical Processes and Improvements for Simulating and Predicting Net Primary Production Over the Tibetan Plateau
Authors: Sun Guodong , Mu Mu , You Qinglong 
Published: November 2020

AbstractThere are still considerable uncertainties related to the numerical simulation and prediction of net primary production (NPP) as an important part of terrestrial carbon sources and sinks over the Tibetan Plateau (TP). To reduce the uncertainty of numerical simulations and improve the ability of predictions, the key physical processes related to the uncertainty of simulated NPP are identified at nine observational stations over the TP. A sensitivity analysis of parameter combinations based on the Conditional Nonlinear Optimal Perturbation related to Parameters (CNOP‐P) approach, which can be used to assess the sensitivity of a parameter subset, is conducted for 28 target physical parameters in the Lund‐Potsdam‐Jena (LPJ) Wetland Hydrology and Methane Dynamic Global Vegetation Model (LPJ‐WHyMe v1.3.1). Firstly, the numerical results show that the uncertainties of physical parameters do lead to a large error in the simulated NPP over the TP, and the range of error varies from 72.4 (MS 3478) to 150.5 g C m−2 year−1 (Ngari station). Secondly, in areas of moderate precipitation over the TP, the photosynthesis is the main factor leading to high uncertainty in NPP modeling. In areas of low and high precipitation over the TP, the combined influences of hydrological processes and photosynthesis play a key role. Finally, eliminating the errors associated with the most sensitive and important parameter combinations led to the maximum benefit in terms of reducing the uncertainty of simulated NPP, when compared to that obtained with the traditional method. This study suggests that we should prioritize reducing the uncertainty of relatively sensitive parameter combinations among all physical parameters to improve the prediction or simulation ability of NPP over the TP.
A Bigger Picture of how the Tibetan Lakes Have Changed Over the Past Decade Revealed by CryoSat‐2 Altimetry
Authors: Jiang Liguang , Nielsen Karina , Andersen Ole B. , Bauer‐Gottwein Peter 
Published: November 2020

AbstractTibetan lakes are an effective indicator of climate change as they are highly sensitive to and directly affected by climate change. The past decade has seen the seven warmest years on record globally. Such observations have prompted questions about lake changes over the Tibetan Plateau. The dense coverage of the CryoSat‐2 altimeter reveals large‐scale patterns in this climate change signal. We investigate lake level variations of more than 200 lakes using altimetry observations from CryoSat‐2 during the period 2010 to 2019. Combined with GRACE/GRACE‐FO, we evaluate the water storage change of lakes and terrestrial water storage (TWS). We find that most studied lakes generally went through three phases of change, that is, rising‐hiatus/decline‐rising, albeit lakes in the north Tibetan Plateau, show higher rising rates. Results also show that lake levels are widely affected by the 2015/16 El Niño event across the entire Inner Plateau via reduced precipitation. Above normal precipitation during 2016–2018, resulted in a sharp rise of ~1.22 m on average, accounting for 56% of the decadal lake level rise (mean/median: 2.19/1.85 m). TWS in the Inner Tibetan Plateau accumulated a net gain of 70.5 km3, which is dominated by the net gain of lake water storage (ca. 63.3 km3). The interannual TWS variation is found to be associated mainly with precipitation. In particular, extreme conditions such as the 2015/16 El Niño, had a profound negative impact on the TWS. The findings in this study shed new light on the response of Tibetan lakes to recent decadal environmental changes.
The FastEddy® Resident‐GPU Accelerated Large‐Eddy Simulation Framework: Model Formulation, Dynamical‐Core Validation and Performance Benchmarks
Authors: Sauer Jeremy A. , Muñoz‐Esparza Domingo 
Published: November 2020

AbstractThis paper introduces a new large‐eddy simulation model, FastEddy®, purpose built for leveraging the accelerated and more power‐efficient computing capacity of graphics processing units (GPUs) toward adopting microscale turbulence‐resolving atmospheric boundary layer simulations into future numerical weather prediction activities. Here a basis for future endeavors with the FastEddy® model is provided by describing the model dry dynamics formulation and investigating several validation scenarios that establish a baseline of model predictive skill for canonical neutral, convective, and stable boundary layer regimes, along with boundary layer flow over heterogeneous terrain. The current FastEddy® GPU performance and efficiency gains versus similarly formulated, state‐of‐the‐art CPU‐based models is determined through scaling tests as 1 GPU to 256 CPU cores. At this ratio of GPUs to CPU cores, FastEddy® achieves 6 times faster prediction rate than commensurate CPU models under equivalent power consumption. Alternatively, FastEddy® uses 8 times less power at this ratio under equivalent CPU/GPU prediction rate. The accelerated performance and efficiency gains of the FastEddy® model permit more broad application of large‐eddy simulation to emerging atmospheric boundary layer research topics through substantial reduction of computational resource requirements and increase in model prediction rate.
What Role Does Hydrological Science Play in the Age of Machine Learning?
Authors: Nearing Grey S. , Kratzert Frederik , Sampson Alden Keefe , Pelissier Craig S. , Klotz Daniel , Frame Jonathan M. , Prieto Cristina , Gupta Hoshin V. 
Published: November 2020

AbstractThis paper is derived from a keynote talk given at the Google's 2020 Flood Forecasting Meets Machine Learning Workshop. Recent experiments applying deep learning to rainfall‐runoff simulation indicate that there is significantly more information in large‐scale hydrological data sets than hydrologists have been able to translate into theory or models. While there is a growing interest in machine learning in the hydrological sciences community, in many ways, our community still holds deeply subjective and nonevidence‐based preferences for models based on a certain type of “process understanding” that has historically not translated into accurate theory, models, or predictions. This commentary is a call to action for the hydrology community to focus on developing a quantitative understanding of where and when hydrological process understanding is valuable in a modeling discipline increasingly dominated by machine learning. We offer some potential perspectives and preliminary examples about how this might be accomplished.
Scaling Analysis of Two‐Phase Flow in Fractal Permeability Fields
Authors: Wang Yuhang , Mckinzie Jesse , Furtado Frederico , Aryana Saman A. 
Published: November 2020

AbstractFluid mixing in permeable media is essential in many practical applications. The mixing process is a consequence of velocity fluctuations owing to geological heterogeneities and mobility contrast of fluids. Heterogeneities in natural rocks are often spatially correlated, and their properties, such as permeability, may be described using fractal distributions. This work models the fractal characteristics of such permeability fields in which the covariance function is expressed as a power‐law function. A generalized scaling relation is derived relating various fractal permeability fields using the magnitude of their fluctuations. This relation reveals the self‐similar behavior of two‐phase flow in such permeable media. To that end, a recently developed, high‐resolution numerical simulator is employed to validate the analytically derived scaling relations. Two flow problems are considered in which flow is governed by (1) a linear and (2) a nonlinear transport equation. Due to the probabilistic representation of the fractal permeability fields, a sensitivity study is conducted for each flow scenario to determine the number of realizations required for statistical convergence. Scaling analysis is performed using ensemble averages of simulated saturation profiles and their mixing lengths. Results support the validity of the developed scaling relation across the range of investigated flow conditions at intermediate times. The dynamics of linear flow in the asymptotic regime is affected by the correlation structure of heterogeneity. In nonlinear flow, scaling behavior appears to be dominated by the degree of nonlinearity.
Preparing for a future COVID-19 wave: insights and limitations from a data-driven evaluation of non-pharmaceutical interventions in Germany
Authors: Aravindakshan Ashwin , Boehnke Jörn , Gholami Ehsan , Nayak Ashutosh 
Published: November 2020

AbstractTo contain the COVID-19 pandemic, governments introduced strict Non-Pharmaceutical Interventions (NPI) that restricted movement, public gatherings, national and international travel, and shut down large parts of the economy. Yet, the impact of the enforcement and subsequent loosening of these policies on the spread of COVID-19 is not well understood. Accordingly, we measure the impact of NPIs on mitigating disease spread by exploiting the spatio-temporal variations in policy measures across the 16 states of Germany. While this quasi-experiment does not allow for causal identification, each policy’s effect on reducing disease spread provides meaningful insights. We adapt the Susceptible–Exposed–Infected–Recovered model for disease propagation to include data on daily confirmed cases, interstate movement, and social distancing. By combining the model with measures of policy contributions on mobility reduction, we forecast scenarios for relaxing various types of NPIs. Our model finds that in Germany policies that mandated contact restrictions (e.g., movement in public space limited to two persons or people co-living), closure of educational institutions (e.g., schools), and retail outlet closures are associated with the sharpest drops in movement within and across states. Contact restrictions appear to be most effective at lowering COVID-19 cases, while border closures appear to have only minimal effects at mitigating the spread of the disease, even though cross-border travel might have played a role in seeding the disease in the population. We believe that a deeper understanding of the policy effects on mitigating the spread of COVID-19 allows a more accurate forecast of disease spread when NPIs are partially loosened and gives policymakers better data for making informed decisions.