bluuebunny/crossref_metadata_2025_split huggingface dataset:
Thermo-catalytic studies on a mixture of plastic waste and biomass
Authors: Sebestyén Zoltán , Blazsó Marianne , Jakab Emma , Miskolczi Norbert , Bozi János , Czégény Zsuzsanna 
Published: July 2021

AbstractThe effects of various catalysts on the composition of volatile pyrolysis products of a plastic waste and biomass mixture (1:1) were studied, by pyrolyzing the mixture sample using slow and fast heating rate. Various zeolite catalysts (β-and Y-zeolites, ZSM-5 and FCC) and nickel-molybdenum catalyst on alumina support were applied to find suitable catalysts for upgrading the quality of the thermal decomposition products of the waste mixture. A sample to catalyst ratio of 2:1 was used in the experiments. The rate of evolution of the decomposition products under slow pyrolysis was measured by thermogravimetry/mass spectrometry (TG/MS). The composition of the pyrolyzates was analyzed in detail by pyrolysis–gas chromatography/mass spectrometry (Py-GC/MS) method. The influence of all applied catalysts was more pronounced on the plastic content of the sample than on biomass. The pyrolysis experiments revealed that the catalysts promoted the cracking reactions of the evolved hydrocarbons; furthermore, the formation of aromatic products was enhanced remarkably in the presence of all zeolite catalysts. Beta-zeolite and ZSM-5 catalysts were found the most effective in cracking hydrocarbons to gaseous products and in aromatization, while the highest CO2 formation was obtained by FCC from the biomass part of the studied waste mixture. NiMo catalyst promoted the H2 production from the plastic part; furthermore, slight aromatization and cracking effects were also observed.
Neuroregulatory role of ginkgolides
Authors: Gachowska Martyna , Szlasa Wojciech , Saczko Jolanta , Kulbacka Julita 
Published: July 2021

AbstractThe application of ginkgolides as a herbal remedy reaches ancient China. Over time many studies confirmed the neuroprotective effect of standard Ginkgo biloba tree extract—the only available ginkgolide source. Ginkgolides present a wide variety of neuroregulatory properties, commonly used in the therapy process of common diseases, such as Alzheimer’s, Parkinson’s, and many other CNS-related diseases and disorders. The neuroregulative properties of ginkgolides include the conditioning of neurotransmitters action, e.g., glutamate or dopamine. Besides, natural compounds induce the inhibition of platelet-activating factors (PAF). Furthermore, ginkgolides influence the inflammatory process. This review focuses on the role of ginkgolides as neurotransmitters or neuromodulators and overviews their impact on the organism at the molecular, cellular, and physiological levels. The clinical application of ginkgolides is discussed as well.
The Core of the Global Corporate Network
Authors: Giglio Ricardo , Lux Thomas 
Published: July 2021

AbstractWe investigate the network topology of a comprehensive data set of the world-wide population of corporate entities. In particular, we have extracted information on the boards of all companies listed in Bloomberg’s archive of company profiles in October, 2015, a total of almost 100,000 firms. We provide information on board membership overlaps at various levels, and, in particular, show that there exists a core of directors who accumulate a large number of seats and are highly connected among themselves both at the level of national networks and at the worldwide aggregated level.
Portability of semantic and spatial–temporal machine learning methods to analyse social media for near-real-time disaster monitoring
Authors: Havas Clemens , Resch Bernd 
Published: July 2021

AbstractUp-to-date information about an emergency is crucial for effective disaster management. However, severe restrictions impede the creation of spatiotemporal information by current remote sensing-based monitoring systems, especially at the beginning of a disaster. Multiple publications have shown promising results in complementing monitoring systems through spatiotemporal information extracted from social media data. However, various monitoring system criteria, such as near-real-time capabilities or applicability for different disaster types and use cases, have not yet been addressed. This paper presents an improved version of a recently proposed methodology to identify disaster-impacted areas (hot spots and cold spots) by combining semantic and geospatial machine learning methods. The process of identifying impacted areas is automated using semi-supervised topic models for various kinds of natural disasters. We validated the portability of our approach through experiments with multiple natural disasters and disaster types with differing characteristics, whereby one use case served to prove the near-real-time capability of our approach. We demonstrated the validity of the produced information by comparing the results with official authority datasets provided by the United States Geological Survey and the National Hurricane Centre. The validation shows that our approach produces reliable results that match the official authority datasets. Furthermore, the analysis result values are shown and compared to the outputs of the remote sensing-based Copernicus Emergency Management Service. The information derived from different sources can thus be considered to reliably detect disaster-impacted areas that were not detected by the Copernicus Emergency Management Service, particularly in densely populated cities.
Disagreement and Deliberation: Evidence from Three Deliberative Mini-Publics
Authors: Grönlund Kimmo , Herne Kaisa , Strandberg Kim , Söderlund Peter 
Published: July 2021

AbstractThis article is based on three experiments in citizen deliberation. We ask whether disagreement at group level as well as at individual level influence participants’ experiences of deliberation. In all three experiments, participants discussed in small groups and answered surveys before and after deliberations. The experiments were population-based with random selection. The topic of the first deliberation was nuclear power, the second dealt with immigration, and the third concerned policies for a language spoken by a national minority. The degree of group level disagreement was subject to experimental manipulation. In the first experiment, all the participants discussed in groups with mixed opinions. In the second experiment, participants were first categorized according to their baseline views, and then randomly allocated into either mixed or like-minded groups. In the third experiment, everyone discussed in like-minded groups. A trained facilitator moderated all small group discussions in the first two experiments. In the language experiment, the participants were randomly assigned into two treatments: groups with both moderation and deliberative norms, and ‘placebo’ groups. Our dependent variables consist of participants’ self-reported experiences of being heard in the discussion, and their feelings of mutual respect. The results show that all participants—regardless of group level disagreement—tend to be satisfied with deliberation. The only exception is the first experiment, where disagreement decreased process satisfaction slightly. At the individual level, participants’ deviation from the group mean had almost no effect.
Minimal important change (MIC): a conceptual clarification and systematic review of MIC estimates of PROMIS measures
Authors: Terwee Caroline B. , Peipert John Devin , Chapman Robert , Lai Jin-Shei , Terluin Berend , Cella David , Griffiths Pip , Mokkink Lidwine B. 
Published: July 2021

AbstractWe define the minimal important change (MIC) as a threshold for a minimal within-person change over time above which patients perceive themselves importantly changed. There is a lot of confusion about the concept of MIC, particularly about the concepts of minimal important change and minimal detectable change, which questions the validity of published MIC values. The aims of this study were: (1) to clarify the concept of MIC and how to use it; (2) to provide practical guidance for estimating methodologically sound MIC values; and (3) to improve the applicability of PROMIS by summarizing the available evidence on plausible PROMIS MIC values. We discuss the concept of MIC and how to use it and provide practical guidance for estimating MIC values. In addition, we performed a systematic review in PubMed on MIC values of any PROMIS measure from studies using recommended approaches. A total of 50 studies estimated the MIC of a PROMIS measure, of which 19 studies used less appropriate methods. MIC values of the remaining 31 studies ranged from 0.1 to 12.7 T-score points. We recommend to use the predictive modeling method, possibly supplemented with the vignette-based method, in future MIC studies. We consider a MIC value of 2–6 T-score points for PROMIS measures reasonable to assume at this point. For surgical interventions a higher MIC value might be appropriate. We recommend more high-quality studies estimating MIC values for PROMIS.
Carboxymethylation of cinnamylalcohol with dimethyl carbonate over the slag-based catalysts
Authors: Kholkina Ekaterina , Kumar Narendra , Eränen Kari , Russo Vincenzo , Rahkila Jani , Peurla Markus , Wärnå Johan , Lehtonen Juha , Murzin Dmitry Yu. 
Published: July 2021

AbstractThe carboxymethylation of cinnamylalcohol with dimethyl carbonate was performed using low-cost catalysts obtained from desulfurization slag. Processing of steel slag performed by different techniques was resulted in a wide range of the catalysts with different morphological and structural properties. Catalytic evaluation of the slag catalysts illustrated diversity of the obtained results strongly dependent on the surface area, crystal morphology and basicity. Catalytic materials demonstrated high variability of the conversion (8–85%) exhibiting similar selectivity to the desired product – cinnamyl methyl carbonate (ca. 80%). A significant impact of ultrasonication on catalytic activity was observed. Comparison of the synthesized samples with commercial basic materials illustrated competitive ability of the slag catalysts. Based on the results of catalytic evaluation and product analysis the reaction network was proposed and verified by thermodynamic analysis. A kinetic model was developed to describe concentration dependencies in carboxymethylation.
Analysing the evolution of computer science events leveraging a scholarly knowledge graph: a scientometrics study of top-ranked events in the past decade
Authors: Lackner Arthur , Fathalla Said , Nayyeri Mojtaba , Behrend Andreas , Manthey Rainer , Auer Sören , Lehmann Jens , Vahdati Sahar 
Published: July 2021

AbstractThe publish or perish culture of scholarly communication results in quality and relevance to be are subordinate to quantity. Scientific events such as conferences play an important role in scholarly communication and knowledge exchange. Researchers in many fields, such as computer science, often need to search for events to publish their research results, establish connections for collaborations with other researchers and stay up to date with recent works. Researchers need to have a meta-research understanding of the quality of scientific events to publish in high-quality venues. However, there are many diverse and complex criteria to be explored for the evaluation of events. Thus, finding events with quality-related criteria becomes a time-consuming task for researchers and often results in an experience-based subjective evaluation. OpenResearch.org is a crowd-sourcing platform that provides features to explore previous and upcoming events of computer science, based on a knowledge graph. In this paper, we devise an ontology representing scientific events metadata. Furthermore, we introduce an analytical study of the evolution of Computer Science events leveraging the OpenResearch.org knowledge graph. We identify common characteristics of these events, formalize them, and combine them as a group of metrics. These metrics can be used by potential authors to identify high-quality events. On top of the improved ontology, we analyzed the metadata of renowned conferences in various computer science communities, such as VLDB, ISWC, ESWC, WIMS, and SEMANTiCS, in order to inspect their potential as event metrics.
Cross-country learning from patents: an analysis of citations flows in innovation trajectories
Authors: Giglio Carlo , Sbragia Roberto , Musmanno Roberto , Palmieri Roberto 
Published: July 2021

AbstractThis study proposes a methodological approach to investigate cross-country creativity/knowledge flows by analyzing patent citation networks, taking the aircraft, aviation and cosmonautics (AAC) industry as a case study. It aims at shedding some light on the following research questions: (a) how cross-country creative/learning flows can be investigated; (b) have countries of current patent owners benefited from patent acquisitions. In fact, despite the well-established economic interest for (analyzing and forecasting) innovation trajectories, this research area is still unexplored, thus, motivating the need for such study. Over 43,000,000 patents have been analyzed whereby: (a) owners have performed cross-country patent acquisitions; (b) acquired patents (granted within 2005–2009) are cited by subsequent patents (2010–2015). Methodology and results are scalable to other industries and can be exploited by managers and policy makers to: (a) help firms forecasting innovation trajectories; (b) support governments in designing/implementing measures nurturing patented innovations in industries deemed relevant to national interest.
News media attention in Climate Action: latent topics and open access
Authors: Dehdarirad Tahereh , Karlsson Kalle 
Published: July 2021

AbstractIn this study we investigated whether open access could assist the broader dissemination of scientific research in Climate Action (Sustainable Development Goal 13) via news outlets. We did this by comparing (i) the share of open and non-open access documents in different Climate Action topics, and their news counts, and (ii) the mean of news counts for open access and non-open access documents. The data set of this study comprised 70,206 articles and reviews in Sustainable Development Goal 13, published during 2014–2018, retrieved from SciVal. The number of news mentions for each document was obtained from Altmetrics Details Page API using their DOIs, whereas the open access statuses were obtained using Unpaywall.org. The analysis in this paper was done using a combination of (Latent Dirichlet allocation) topic modelling, descriptive statistics, and regression analysis. The covariates included in the regression analysis were features related to authors, country, journal, institution, funding, readability, news source category and topic. Using topic modelling, we identified 10 topics, with topics 4 (meteorology) [21%], 5 (adaption, mitigation, and legislation) [18%] and 8 (ecosystems and biodiversity) [14%] accounting for 53% of the research in Sustainable Development Goal 13. Additionally, the results of regression analysis showed that while keeping all the variables constant in the model, open access papers in Climate Action had a news count advantage (8.8%) in comparison to non-open access papers. Our findings also showed that while a higher share of open access documents in topics such as topic 9 (Human vulnerability to risks) might not assist with its broader dissemination, in some others such as topic 5 (adaption, mitigation, and legislation), even a lower share of open access documents might accelerate its broad communication via news outlets.
Lattice Boltzmann Modeling of Drying of Porous Media Considering Contact Angle Hysteresis
Authors: Qin Feifei , Zhao Jianlin , Kang Qinjun , Derome Dominique , Carmeliet Jan 
Published: July 2021

AbstractDrying of porous media is governed by a combination of evaporation and movement of the liquid phase within the porous structure. Contact angle hysteresis induced by surface roughness is shown to influence multi-phase flows, such as contact line motion of droplet, phase distribution during drainage and coffee ring formed after droplet drying in constant contact radius mode. However, the influence of contact angle hysteresis on liquid drying in porous media is still an unanswered question. Lattice Boltzmann model (LBM) is an advanced numerical approach increasingly used to study phase change problems including drying. In this paper, based on a geometric formulation scheme to prescribe contact angle, we implement a contact angle hysteresis model within the framework of a two-phase pseudopotential LBM. The capability and accuracy of prescribing and automatically measuring contact angles over a large range are tested and validated by simulating droplets sitting on flat and curved surfaces. Afterward, the proposed contact angle hysteresis model is validated by modeling droplet drying on flat and curved surfaces. Then, drying of two connected capillary tubes is studied, considering the influence of different contact angle hysteresis ranges on drying dynamics. Finally, the model is applied to study drying of a dual-porosity porous medium, where phase distribution and drying rate are compared with and without contact angle hysteresis. The proposed model is shown to be capable of dealing with different contact angle hysteresis ranges accurately and of capturing the physical mechanisms during drying in different porous media including flat and curved geometries.
Shape My Face: Registering 3D Face Scans by Surface-to-Surface Translation
Authors: Bahri Mehdi , O’ Sullivan Eimear , Gong Shunwang , Liu Feng , Liu Xiaoming , Bronstein Michael M. , Zafeiriou Stefanos 
Published: July 2021

AbstractStandard registration algorithms need to be independently applied to each surface to register, following careful pre-processing and hand-tuning. Recently, learning-based approaches have emerged that reduce the registration of new scans to running inference with a previously-trained model. The potential benefits are multifold: inference is typically orders of magnitude faster than solving a new instance of a difficult optimization problem, deep learning models can be made robust to noise and corruption, and the trained model may be re-used for other tasks, e.g. through transfer learning. In this paper, we cast the registration task as a surface-to-surface translation problem, and design a model to reliably capture the latent geometric information directly from raw 3D face scans. We introduce Shape-My-Face (SMF), a powerful encoder-decoder architecture based on an improved point cloud encoder, a novel visual attention mechanism, graph convolutional decoders with skip connections, and a specialized mouth model that we smoothly integrate with the mesh convolutions. Compared to the previous state-of-the-art learning algorithms for non-rigid registration of face scans, SMF only requires the raw data to be rigidly aligned (with scaling) with a pre-defined face template. Additionally, our model provides topologically-sound meshes with minimal supervision, offers faster training time, has orders of magnitude fewer trainable parameters, is more robust to noise, and can generalize to previously unseen datasets. We extensively evaluate the quality of our registrations on diverse data. We demonstrate the robustness and generalizability of our model with in-the-wild face scans across different modalities, sensor types, and resolutions. Finally, we show that, by learning to register scans, SMF produces a hybrid linear and non-linear morphable model. Manipulation of the latent space of SMF allows for shape generation, and morphing applications such as expression transfer in-the-wild. We train SMF on a dataset of human faces comprising 9 large-scale databases on commodity hardware.
Planning and Suitability Assessment of Large-scale Nature-based Solutions for Flood-risk Reduction
Authors: Mubeen Adam , Ruangpan Laddaporn , Vojinovic Zoran , Sanchez Torrez Arlex , Plavšić Jasna 
Published: July 2021

AbstractAdverse effects of climate change are increasing around the world and the floods are posing significant challenges for water managers. With climate projections showing increased risks of storms and extreme precipitation, the use of traditional measures alone is no longer an option. Nature-Based Solutions (NBS) offer a suitable alternative to reduce the risk of flooding and provide multiple benefits. However, planning such interventions requires careful consideration of various factors and local contexts. The present paper provides contribution in this direction and it proposes a methodology for allocation of large-scale NBS using suitability mapping. The methodology was implemented within the toolboxes of ESRI ArcMap software in order to map suitability for four types of NBS interventions: floodplain restoration, detention basins, retention ponds, and river widening. The toolboxes developed were applied to the case study area in Serbia, i.e., the Tamnava River basin. Flood maps were used to determine the volume of floodwater that needs to be stored for reducing flood risk in the basin and subsequent downstream areas. The suitability maps produced indicate the potential of the new methodology and its application as a decision-support tool for selection and allocation of large-scale NBS.
Retention of eucalyptus harvest residues reduces soil compaction caused by deep subsoiling
Authors: de Melo Raphael Oliveira , da Fonseca Aymbiré Angeletti , de Barros Nairam Félix , Fernandes Raphael Bragança Alves , Teixeira Rafael da Silva , Melo Iago Nery , Martins Ricardo Previdente 
Published: July 2021

AbstractEucalyptus harvesting, forwarding and soil tillage operations are among the main causes for compaction of forest soils, with potential impacts on productivity. This concern is especially important in areas with soils that are naturally compacted (fragipans and duripans). In these soils, tillage operations include the use of subsoilers that can reach depths of more than one meter and require heavy tractors that exert high pressure on the soil. One of the ways to try to minimize the effect of this compaction is by retaining harvest residues. The objective of this study was to evaluate the impacts of eucalyptus harvesting on soil physical attributes, as well as to determine the potential of different types of residue management to reduce compaction from the soil tillage operation. Two experiments were conducted in the same area with a Yellow Argisol. In the first experiment, compaction caused by mechanized harvesting with harvester + forwarder was evaluated. In the second experiment, different managements of harvest residues were examined as potential modifiers of soil compaction during tillage for new plantings. For this, three managements systems were tested: (1) retention of all harvest residues and litter from the previous rotation (HR + L), (2) retention of litter from the previous rotation (L), and (3) removal of harvest residues and litter from the previous rotation (WR). Before and after harvest, sampling was carried out in the planting rows and inter-rows, and after tillage, samples were collected in the traffic line of the subsoiler-tractor set. In both experiments, undisturbed soil samples were collected from the center of the 0–10, 10–20, 20–40, 40–60, and 60–100 cm layers to determine soil density and total porosity. In each period and site of evaluation, mechanical resistance to penetration up to the 60-cm depth was also determined. The harvesting operation increased soil density at 0–10 and 60–100 cm depths only in the inter-rows. Retention of harvest residues and litter (HR + L) after harvesting avoided increases in soil density and penetration resistance caused by machine traffic during tillage. The results indicate the importance of retaining harvest residues on forest soils for achieving sustainable utilization and for conserving soil quality.