bluuebunny/crossref_metadata_2025_split huggingface dataset:
Social Capital and Mental Health Among Black and Minority Ethnic Groups in the UK
Authors: Bamford Jordan , Klabbers Gonnie , Curran Emma , Rosato Michael , Leavey Gerard 
Published: July 2020

AbstractBlack and minority ethnic communities are at higher risk of mental health problems. We explore differences in mental health and the influence of social capital among ethnic minority groups in Great Britain. Cross-sectional linear and logistic regression analysis of data from Wave 6 (2014–2016) of the Understanding Society databases. In unadjusted models testing the likelihood of reporting psychological distress (i) comparing against a white (British) reference population Indian, Pakistani, Bangladeshi and mixed ethnic minority groups recorded excess levels of distress; and (ii) increasing levels of social capital recorded a strong protective effect (OR = 0.94: 95% CI 0.935, 0.946). In a subsequent series of gender-specific incremental logistic models-after adjustment for sociodemographic and socioeconomic factors Pakistani (males and females) and Indian females recorded higher likelihoods of psychological distress, and the further inclusion of social capital in these models did not materially alter these results. More research on the definition, measurement and distribution of social capital as applies to ethnic minority groups in Great Britain, and how it influences mental wellbeing is needed.
Predicting technical debt from commit contents: reproduction and extension with automated feature selection
Authors: Rantala Leevi , Mäntylä Mika 
Published: July 2020

AbstractSelf-admitted technical debt refers to sub-optimal development solutions that are expressed in written code comments or commits. We reproduce and improve on a prior work by Yan et al. (2018) on detecting commits that introduce self-admitted technical debt. We use multiple natural language processing methods: Bag-of-Words, topic modeling, and word embedding vectors. We study 5 open-source projects. Our NLP approach uses logistic Lasso regression from Glmnet to automatically select best predictor words. A manually labeled dataset from prior work that identified self-admitted technical debt from code level commits serves as ground truth. Our approach achieves + 0.15 better area under the ROC curve performance than a prior work, when comparing only commit message features, and + 0.03 better result overall when replacing manually selected features with automatically selected words. In both cases, the improvement was statistically significant (p< 0.0001). Our work has four main contributions, which are comparing different NLP techniques for SATD detection, improved results over previous work, showing how to generate generalizable predictor words when using multiple repositories, and producing a list of words correlating with SATD. As a concrete result, we release a list of the predictor words that correlate positively with SATD, as well as our used datasets and scripts to enable replication studies and to aid in the creation of future classifiers.
Greenhouse gas emissions convergence in Spain: evidence from the club clustering approach
Authors: Apergis Nicholas , Garzón Antonio J. 
Published: July 2020

AbstractThis study examines the convergence of greenhouse gas emissions per capita across the 19 Spanish regions using the Phillips-Sul club convergence approach over the period spanning from 1990 to 2017. The results indicate the presence of four clubs which converge to different equilibria in emissions per capita and three clubs in terms of income per capita, which involves different regions. These findings suggest that mitigation policies should explicitly consider the presence of different clubs of regions with different convergence paths in terms of emissions and income per capita and address the distributional effect of transfers across regions.
Effects of Ferrous Iron and Hydrogen Sulfide on Nitrate Reduction in the Sediments of an Estuary Experiencing Hypoxia
Authors: Jäntti Helena , Aalto Sanni L. , Paerl Hans W. 
Published: July 2020

AbstractHypoxia is common feature of eutrophic estuaries and semi-enclosed seas globally. One of the key factors driving hypoxia is nitrogen pollution. To gain more insight into the effects of hypoxia on estuarine nitrogen cycling, we measured potential nitrate reduction rates at different salinities and levels of hypoxia in a eutrophic temperate microtidal estuary, the Neuse River Estuary, North Carolina, USA. We also tested the effect of hydrogen sulfide and ferrous iron additions on the nitrate reduction pathways. Overall, DNRA dominated over denitrification in this periodically hypoxic estuary and there was no correlation between the potential nitrate reduction rates, salinity, or dissolved oxygen. However, when hypoxia lasted several months, denitrification capacity was almost completely lost, and nearly all nitrate added to the sediment was reduced via DNRA. Additions of hydrogen sulfide stimulated DNRA over denitrification. Additions of ferrous iron stimulated nitrate consumption; however, the end product of nitrate consumption was not clear. Interestingly, substantial nitrous oxide formation occurred in sediments that had experienced prolonged hypoxia and were amended with nitrate. Given expanding hypoxia predicted with climate change scenarios and the increasing nitrate loads to coastal systems, coastal sediments may lose their capability to mitigate nitrogen pollution due to DNRA dominating over denitrification during extended hypoxic periods.
Effect of small-scale heterogeneity on biopolymer performance in carbonates
Authors: Al-Shalabi Emad W. 
Published: July 2020

AbstractPolymer flooding is a well-established chemical method for enhancing oil recovery in sandstones; however, it has a limited application in carbonates. This is due to the harsh reservoir conditions in carbonates including high temperature, high salinity, and high heterogeneity with low permeability. This paper numerically investigates the effect of Schizophyllan biopolymer on oil recovery from carbonates. The effect of biopolymer on oil recovery was predicted by running several 1D simulations. Biopolymer flow behavior was modeled based on experimental data. The results showed that the effect of the investigated biopolymer on oil recovery was not much pronounced compared to conventional waterflooding. This is due to small-scale heterogeneity, which increased effective shear rate and hence, decreased in-situ polymer viscosity. Formation permeability, polymer viscosity, and oil saturation maps were consistent in justifying this observation. The findings of this study were supported by fractional flow and mobility ratio analyses. This work highlights the importance of small-scale heterogeneity of the core in modeling polymer flooding, particularly the shear effect on polymer viscosity.
Reasoning in Description Logic Ontologies for Privacy Management
Authors: Nuradiansyah Adrian 
Published: July 2020

AbstractThis work is initially motivated by a privacy scenario in which the confidential information about persons or its properties formulated in description logic (DL) ontologies should be kept hidden. We investigate procedures to detect whether this confidential information can be disclosed in a certain situation by using DL formalisms. If it is the case that this information can be deduced from the ontologies, which implies certain privacy policies are not fulfilled, then one needs to consider methods to repair these ontologies in a minimal way such that the modified ontologies complies with the policies. However, privacy compliance itself is not enough if a possible attacker can also obtain relevant information from other sources, which together with the modified ontologies might violate the privacy policy. This article provides a summary of studies and results from Adrian Nuradiansyah’s Ph.D. dissertation that are corresponding to the addressed problem above with a special emphasis on the investigations on the worst-case complexities of those problems as well as the complexity of the procedures and algorithms solving the problems.
Genetic and genomic resources, and breeding for accelerating improvement of small millets: current status and future interventions
Authors: Vetriventhan M. , Azevedo Vania C. R. , Upadhyaya H. D. , Nirmalakumari A. , Kane-Potaka Joanna , Anitha S. , Ceasar S. Antony , Muthamilarasan M. , Bhat B. Venkatesh , Hariprasanna K. , Bellundagi Amasiddha , Cheruku Deepika , Backiyalakshmi C. , Santra Dipak , Vanniarajan C. , Tonapi Vilas A. 
Published: July 2020

AbstractCurrent agricultural and food systems encourage research and development on major crops, neglecting regionally important minor crops. Small millets include a group of small- seeded cereal crops of the grass family Poaceae. This includes finger millet, foxtail millet, proso millet, barnyard millet, kodo millet, little millet, teff, fonio, job’s tears, guinea millet, and browntop millet. Small millets are an excellent choice to supplement major staple foods for crop and dietary diversity because of their diverse adaptation on marginal lands, less water requirement, lesser susceptibility to stresses, and nutritional superiority compared to major cereal staples. Growing interest among consumers about healthy diets together with climate-resilient features of small millets underline the necessity of directing more research and development towards these crops. Except for finger millet and foxtail millet, and to some extent proso millet and teff, other small millets have received minimal research attention in terms of development of genetic and genomic resources and breeding for yield enhancement. Considerable breeding efforts were made in finger millet and foxtail millet in India and China, respectively, proso millet in the United States of America, and teff in Ethiopia. So far, five genomes, namely foxtail millet, finger millet, proso millet, teff, and Japanese barnyard millet, have been sequenced, and genome of foxtail millet is the smallest (423-510 Mb) while the largest one is finger millet (1.5 Gb). Recent advances in phenotyping and genomics technologies, together with available germplasm diversity, could be utilized in small millets improvement. This review provides a comprehensive insight into the importance of small millets, the global status of their germplasm, diversity, promising germplasm resources, and breeding approaches (conventional and genomic approaches) to accelerate climate-resilient and nutrient-dense small millets for sustainable agriculture, environment, and healthy food systems.
Menthol as an Ergogenic Aid for the Tokyo 2021 Olympic Games: An Expert-Led Consensus Statement Using the Modified Delphi Method
Authors: Barwood M. J. , Gibson O. R. , Gillis D. J. , Jeffries O. , Morris N. B. , Pearce J. , Ross M. L. , Stevens C. , Rinaldi K. , Kounalakis S. N. , Riera F. , Mündel T. , Waldron M. , Best R. 
Published: July 2020

Abstract
Introduction
Menthol topical application and mouth rinsing are ergogenic in hot environments, improving performance and perception, with differing effects on body temperature regulation. Consequently, athletes and federations are beginning to explore the possible benefits to elite sport performance for the Tokyo 2021 Olympics, which will take place in hot (~ 31 °C), humid (70% RH) conditions. There is no clear consensus on safe and effective menthol use for athletes, practitioners, or researchers. The present study addressed this shortfall by producing expert-led consensus recommendations.

Method
Fourteen contributors were recruited following ethical approval. A three-step modified Delphi method was used for voting on 96 statements generated following literature consultation; 192 statements total (96/96 topical application/mouth rinsing). Round 1 contributors voted to “agree” or “disagree” with statements; 80% agreement was required to accept statements. In round 2, contributors voted to “support” or “change” their round 1 unaccepted statements, with knowledge of the extant voting from round 1. Round 3 contributors met to discuss voting against key remaining statements.

Results
Forty-seven statements reached consensus in round 1 (30/17 topical application/rinsing); 14 proved redundant. Six statements reached consensus in round 2 (2/4 topical application/rinsing); 116 statements proved redundant. Nine further statements were agreed in round 3 (6/3 topical application/rinsing) with caveats.

Discussion
Consensus was reached on 62 statements in total (38/24 topical application/rinsing), enabling the development of guidance on safe menthol administration, with a view to enhancing performance and perception in the heat without impairing body temperature regulation.
Artificial bee colony algorithm based on knowledge fusion
Authors: Wang Hui , Wang Wenjun , Zhou Xinyu , Zhao Jia , Wang Yun , Xiao Songyi , Xu Minyang 
Published: July 2020

AbstractArtificial bee colony (ABC) algorithm is one of the branches of swarm intelligence. Several studies proved that the original ABC has powerful exploration and weak exploitation capabilities. Therefore, balancing exploration and exploitation is critical for ABC. Incorporating knowledge in intelligent optimization algorithms is important to enhance the optimization capability. In view of this, a novel ABC based on knowledge fusion (KFABC) is proposed. In KFABC, three kinds of knowledge are chosen. For each kind of knowledge, the corresponding utilization method is designed. By sensing the search status, a learning mechanism is proposed to adaptively select appropriate knowledge. Thirty-two benchmark problems are used to validate the optimization capability of KFABC. Results show that KFABC outperforms nine ABC and three differential evolution algorithms.
Strategies to Promote Biogas Generation and Utilisation from Palm Oil Mill Effluent
Authors: Foong Steve Z. Y. , Chong Mei Fong , Ng Denny K. S. 
Published: July 2020

AbstractPalm oil mills generate a large amount of wastewater, known as palm oil mill effluent, during the production of crude palm oil. The high organic contents in palm oil mill effluent have an excellent potential for biogas utilisation. Besides, such effluent must be further treated before discharge or reused in milling processes. In this respect, an integrated biogas and wastewater treatment system should be developed. The aim of this paper is to synthesise and optimise an integrated biogas and wastewater treatment system via a process systems engineering tool that yields maximum economic performance. To illustrate the proposed approach, a typical palm oil mill case study in Malaysia is presented. The variation in palm oil mill effluent availability is considered to evaluate the changes in performance and ensuring the flexibility of the developed system. As shown in the results, implementation of integrated biogas and wastewater treatment system in a typical 60 t/h mill in Malaysia could export up to 1.9 MW electrical power on average. Alternatively, 110,800 GJ/year of compressed biomethane can be produced when feed-in to the national grid is not available. The implementation of integrated biogas and wastewater treatment system successfully reduces greenhouse gas emissions by 50,430 t CO2e/year as compared with the conventional open ponding system practiced in the industry. Lastly, feasibility studies and strategies to promote biogas utilisation in the industry are performed.
First, They Came for the Old and Demented:
Authors: Sætra Henrik Skaug 
Published: July 2020

AbstractHealth care technology is all the rage, and artificial intelligence (AI) has long since made its inroads into the previously human-dominated domain ofcare. AI is used in diagnostics, but also in therapy and assistance, sometimes in the form of social robots with fur, eyes and programmed emotions. Patient welfare, working conditions for the caretakers and cost-efficiency are routinely said to be improved by employing new technologies. The old with dementia might be provided with a robot seal, or a humanoid companion robot, and if these companions increase the happiness of the patients, why should we not venture down this road? Come to think of it, when we have these machines, why not use them as tutors in our schools and caretakers for our children? More happiness reported, as our children are entertained, well-nourished, well-trained and never alone. Lovely and loving robots have also been made, and happiness abounds when these are provided to lonely adults. Happiness all around, and a hedonistic heaven – the utilitarian’s dream, as reported, or measured, well-being reaches all-time highs. But there is a reason to be wary of this development. The logic that allows this development ultimately leads to the conclusion that we would all be best off if we could simply be wired to a computer that provided us with whatever we needed to feel perfectly satisfied. The care-giving machines are here.
El Niño Detection Via Unsupervised Clustering of Argo Temperature Profiles
Authors: Houghton Isabel A. , Wilson James D. 
Published: July 2020

AbstractVariability in the El Niño‐Southern Oscillation (ENSO) has global impacts on seasonal temperatures and rainfall. Current detection methods for extreme phases, which occur with irregular periodicity, rely upon sea surface temperature anomalies within a strictly defined geographic region of the Pacific Ocean. However, under changing climate conditions and ocean warming, these historically motivated indicators may not be reliable into the future. In this work, we demonstrate the power of data clustering as a robust, automatic way to detect anomalies in climate patterns. Ocean temperature profiles from Argo floats are partitioned into similar groups utilizing unsupervised machine learning methods. The automatically identified groups of measurements represent spatially coherent, large‐scale water masses in the Pacific, despite no inclusion of geospatial information in the clustering task. Further, spatiotemporal dynamics of the clusters are strongly indicative of El Niño events, the east Pacific warming phase of ENSO. The fitting of a cluster model on a collection of ocean profiles identifies changes in the vertical structure of the temperature profiles through reassignment to a different group, concisely capturing physical changes to the water column during an El Niño event, such as thermocline tilting. Clustering proves to be an effective tool for analysis of the irregularly sampled (in space and time) data from Argo floats and may serve as a novel approach for detecting anomalies given the freedom from thresholding decisions. Unsupervised machine learning could be particularly valuable due to its ability to identify patterns in data sets without user‐imposed expectations, facilitating further discovery of anomaly indicators.
Advective Lateral Transport of Streamwise Momentum Governs Mixing at Small River Confluences
Authors: Lewis Quinn , Rhoads Bruce , Sukhodolov Alexander , Constantinescu George 
Published: July 2020

AbstractConfluences are important sites for mixing within river networks. Past work has shown that mixing within confluences is highly variable; in some cases flows mix rapidly and in other cases flows remain unmixed far downstream of the confluence. The fluvial processes that govern mixing within confluences remain poorly understood. This study relates patterns and amounts of mixing to three‐dimensional flow structure at three small confluences. It focuses on lateral fluxes of streamwise momentum, which theoretical considerations suggest should influence lateral mixing. Patterns and amounts of mixing differ at the three sites. Considerable mixing occurs at an asymmetrical confluence with strong helical motion within flow from the lateral tributary, which produces substantial differences in advective lateral transport of streamwise momentum over depth. Minor mixing occurs at a comparatively symmetrical confluence where incoming flows have relatively equal momentum fluxes; however, helical motion within one of the flows locally increases mixing. At a symmetrical confluence where one incoming flow has much greater momentum flux than the other, mixing occurs largely through progressive lateral shifting of the mixing interface toward the minor tributary because of the strong lateral flux of streamwise momentum by the dominant tributary. At all three confluences, lateral turbulent transport of streamwise momentum is an order of magnitude less than advective lateral transport of streamwise momentum. The study indicates that generalization of mixing at confluences remains challenging but that advective lateral fluxes of streamwise momentum related to secondary currents (helical motion) or primary flow (cross currents) greatly enhance mixing at confluences.
An Improved Covariate for Projecting Future Rainfall Extremes?
Authors: Roderick Thomas P. , Wasko Conrad , Sharma Ashish 
Published: July 2020

AbstractProjection of extreme rainfall under climate change remains an area of considerable uncertainty. In the absence of geographically consistent simulations of extreme rainfall for the future, alternatives relying on physical relationships between a warmer atmosphere and its moisture carrying capacity are projected, scaling with a known atmospheric covariate. The most common atmospheric covariate adopted is surface air temperature, as it exhibits great consistency across climate model simulations into the future and, as per the Clausius‐Clapeyron relationship, has a well‐established link to atmospheric moisture capacity. However, empirical assessments of this relationship show that it varies with latitude, surface temperature, atmospheric temperature, and other factors, suggesting there may be more stable “global” atmospheric covariates that could be used instead. We argue that a better‐suited covariate would be one that captures the relationship between extreme rainfall and temperature but exhibits greater consistency in the relationship across regions as well as climatic zones. Our analysis identifies plausible atmospheric indicators of changes to future extreme rainfall, which now proliferate literature and compare their suitability based on the variability they exhibit across multiple geographical, topographic, and climatic zones within Australia. It is shown that surface air temperature exhibits a regionally inconsistent relationship with extreme rainfall and hence is not suitable for projecting to future conditions. The study identified integrated water vapor and surface dew point temperature as promising alternatives, with the former showing greater consistency in space but at the cost of reduced temporal coverage.