bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Bayesian approach to exploring the influence of climate variability modes on fire weather conditions and lightning-ignited wildfires
Authors: Bates Bryson C. , Dowdy Andrew J. , McCaw Lachlan 
Published: April 2021

AbstractUnderstanding the relationships between large-scale, low-frequency climate variability modes, fire weather conditions and lighting-ignited wildfires has implications for fire-weather prediction, fire management and conservation. This article proposes a Bayesian network framework for quantifying the influence of climate modes on fire weather conditions and occurrence of lightning-ignited wildfires. The main objectives are to describe and demonstrate a probabilistic framework for identifying and quantifying the joint and individual relationships that comprise the climate-wildfire system; gain insight into potential causal mechanisms and pathways; gauge the influence of climate modes on fire weather and lightning-ignition relative to that of local-scale conditions alone; assess the predictive skill of the network; and motivate the use of techniques that are intuitive, flexible and for which user‐friendly software is freely available. A case study illustrates the application of the framework to a forested region in southwest Australia. Indices for six climate variability modes are considered along with two hazard variables (observed fire weather conditions and prescribed burn area), and a 41-year record of lightning-ignited wildfire counts. Using the case study data set, we demonstrate that the proposed framework: (1) is based on reasonable assumptions provided the joint density of the variables is converted to multivariate normal; (2) generates a parsimonious and interpretable network architecture; (3) identifies known or partially known relationships between the variables; (4) has potential to be used in a predictive setting for fire weather conditions; and (5) climate modes are more directly related to fire weather conditions than to lightning-ignition counts.
Novel perfluoropolyalkylethers monomers: synthesis and photo-induced cationic polymerization
Authors: Trusiano Giuseppe , Vitale Alessandra , Pulfer Jason , Newton Josiah , Joly-Duhamel Christine , Friesen Chadron M. , Bongiovanni Roberta 
Published: April 2021

AbstractSeveral difunctional oligomers were synthesized by functionalizing perfluoropolyalkylether (PFPAE) chains with different vinyl ethers and epoxides end-groups. Due to their innate synthetic challenges and demanding purification protocols, the PFPAE derivatives were obtained in low yield and with an average functionality lower than 2. However, the functionalized PFPAE oligomers were successful in being used in photo-induced cationic polymerization processes, obtaining transparent and soft films. The influences of the fluorinated chains, and various end-groups on the photopolymerization process were investigated, as well their chemical stability, thermal degradation, and surface properties.
Graphical abstract
N-semble-based method for identifying Parkinson’s disease genes
Authors: Arora Priya , Mishra Ashutosh , Malhi Avleen 
Published: April 2021

AbstractParkinson’s disease (PD) genes identification plays an important role in improving the diagnosis and treatment of the disease. A number of machine learning methods have been proposed to identify disease-related genes, but only few of these methods are adopted for PD. This work puts forth a novel neural network-based ensemble (n-semble) method to identify Parkinson’s disease genes. The artificial neural network is trained in a unique way to ensemble the multiple model predictions. The proposed n-semble method is composed of four parts: (1) protein sequences are used to construct feature vectors using physicochemical properties of amino acid; (2) dimensionality reduction is achieved using the t-Distributed Stochastic Neighbor Embedding (t-SNE) method, (3) the Jaccard method is applied to find likely negative samples from unknown (candidate) genes, and (4) gene prediction is performed with n-semble method. The proposed n-semble method has been compared with Smalter’s, ProDiGe, PUDI and EPU methods using various evaluation metrics. It has been concluded that the proposed n-semble method outperforms the existing gene identification methods over the other methods and achieves significantly higher precision, recall and F Score of 88.9%, 90.9% and 89.8%, respectively. The obtained results confirm the effectiveness and validity of the proposed framework.
Assessment of pedicle size in patients with scoliosis using EOS 2D imaging: a validity and reliability study
Authors: Peeters C. M. M. , van Houten L. , Kempen D. H. R. , Wapstra F. H. , Jutte P. C. , van den Akker-Scheek I. , Faber C. 
Published: April 2021

Abstract
Purpose
Free-hand pedicle screw insertion methods are widely used for screw insertion during scoliosis surgery. Preoperative knowledge about the pedicle size helps to maximize screw containment and minimize the risk of pedicle breach. Radiographs taken by a biplanar low-dose X-ray device (EOS) have no divergence in the vertical plane. The criterion validity and reliability of preoperative EOS images for pedicle size measurements in patients with idiopathic scoliosis (IS) was investigated in this study.

Methods
Sixteen patients who underwent surgical treatment for IS were prospectively included. Intra- and extracortical pedicle height and width measurements on EOS images were compared with reconstructed intra-operative 3D images of the isthmus of included pedicles. Secondly, intra- and interobserver reliability of pedicle size measurements on EOS images was determined.

Results
The total number of analyzed pedicles was 203. The correlation between the EOS and 3D scan measurements was very strong for the intra- and extracortical pedicle height and strong for the intra- and extracortical pedicle width. There are, however, significant, but likely clinically irrelevant differences (mean absolute differences < 0.43 mm) between the two measure methods for all four measurements except for extracortical pedicle height. For pedicles classified as Nash–Moe 0, no significant differences in intra- and extracortical pedicle width were observed. Both intra- and interobserver reliability was excellent for all pedicle size measurements on EOS images.

Conclusion
The results of this study indicate a good validity and reliability for pedicle size measurements on EOS radiographs. Therefore, EOS radiographs may be used for a preoperative estimation of pedicle size and subsequent screw diameter in patients with IS.
Towards approval of autonomous ship systems by their operational envelope
Authors: Rødseth Ørnulf Jan , Lien Wennersberg Lars Andreas , Nordahl Håvard 
Published: April 2021

AbstractCurrent guidelines for approval of autonomous ship systems are focused on the ships’ concrete operations and their geographic area. This is a natural consequence of the link between geography and the navigational complexity, but moving the ship to a new area or changing owners may require a costly re-approval. The automotive industry has introduced the Operational Design Domain (ODD) that can be used as a basis for approval. However, the ODD does not include the human control responsibilities, while most autonomous ship systems are expected to be dependent on sharing control responsibilities between humans and automation. We propose the definition of an operational envelope for autonomous ship systems that include the sharing of responsibilities between human and automation, and that is general enough to allow approval of autonomous ship systems in all geographic areas and operations that falls within the envelope. We also show how the operational envelope can be defined using a system modelling language, such as the unified modelling language (UML).
Overcoming the barriers of mobile health that hamper sustainability in low-resource environments
Authors: Greve Maike , Brendel Alfred Benedikt , van Osten Nils , Kolbe Lutz M. 
Published: April 2021

AbstractAimThis research aims to identify response strategies that non-profit organizations (NPOs) can apply to overcome the barriers that hamper the sustainable use of mobile health (mHealth) interventions in low-resource environments (LREs), such as in Sub-Saharan Africa (SSA).Subject and methodA qualitative study on mHealth initiatives in SSA is conducted through semi-structured interviews with 15 key informants of NPOs that operate and manage mHealth interventions in this region. The interviews focus on identifying existing barriers and response strategies that NPOs apply to enable sustainable and long-term running interventions.ResultsBuilding on grounded theory techniques, the collected data guided us towards a process model that identifies four aggregated categories of challenging areas that require response strategies (economy, environment, technology, and user acceptance).ConclusionThis study provides contributions from and implications for NPOs and researchers. Health practitioners are provided with a knowledge base of what barriers to expect and how to overcome them, to strive for sustainable implementation from the very beginning of an intervention. A process model is identified that structures the response strategies in a time-based agenda of mHealth initiatives and thus makes a theoretical contribution. Overall, this study addresses the need for a theoretical consideration of the “pilotitis” phenomenon, which currently hampers the sustainable implementation and scaling up of mHealth initiatives. While the focus is specifically on mHealth initiatives, the overall findings help prevent discontinuance of projects in the future after the pilot, and help facilitate LREs on their way to sustainable health interventions and universal health coverage.
Nutritional potential of underutilized edible plant species in coffee agroforestry systems of Yayu, southwestern Ethiopia
Authors: Aragaw Habtamu Seyoum , Nohr Donatus , Callo-Concha Daniel 
Published: April 2021

AbstractEthiopia is confronted with the paradox of hosting hundreds of edible plants and having high food and nutritional insecurity. Meals are mainly made up of staples and often lack of protein and micronutrients. Therefore, a large section of the population, particularly children and women, are malnourished. We hypothesize that wild edible plant species can contribute to fulfil the micronutrient demands of local people. Hence, we assessed the nutritional potential of underutilized edible plant species growing in understories of coffee agroforestry systems of southwestern Ethiopia. An ethnobotanical household survey (n = 300) documented the edible existing plants; and a promising subset of them (n = 12) was analysed for nutrient and antinutritional factor content in the lab. All 12 species, except fruits, have higher calcium, iron and zinc contents compared to regularly cultivated crops. Vitamin C was high in Syzygium guineense (330.72 mg/100 g edible parts or EP) and Rubus apetalus (294.19 mg/100 g). Beta-carotene ranged from 9.2 to 75 µg retinol activity equivalent (RAE) /100 g 25 among all species, but was exceptionally high in Rubus apetalus (161.7 µg RAE/100 g). Concerning the antinutritional factors, phytate content varied from 31.06 to 601.65 µg/100 g, being lower in Dioscorea prehensilis (31.06 µg/100 g) and D. alata (90.17 µg/100 g) compared to Carissa spinarum (601.65 µg/100 g) and Solanum nigrum (536.48 µg/100 g). Thus, we conclude that the assessed underutilized species are potential sources of dietary nutrients locally needed, and are notable Amaranthus graecizans, Portulaca oleracea and Dioscorea cayenensis as providers of Ca, Fe and Zn, and the fruit Rubus apetalus of provitamin A.
Economic assessment of the development of CO2 direct reduction technologies in long-term climate strategies of the Gulf countries
Authors: Babonneau Frédéric , Badran Ahmed , Benlahrech Maroua , Haurie Alain , Schenckery Maxime , Vielle Marc 
Published: April 2021

AbstractThis paper proposes an assessment of long-term climate strategies for oil- and gas-producing countries—in particular, the Gulf Cooperation Council (GCC) member states—as regards the Paris Agreement goal of limiting the increase of surface air temperature to 2°C by the end of the twenty-first century. The study evaluates the possible role of carbon dioxide removal (CDR) technologies under an international emissions trading market as a way to mitigate welfare losses. To model the strategic context, one assumes that a global cumulative emissions budget will have been allocated among different coalitions of countries—the GCC being one of them—and the existence of an international emissions trading market. A meta-game model is proposed in which deployment of CDR technologies as well as supply of emission rights are strategic variables and the payoffs are obtained from simulations of a general equilibrium model. The results of the simulations indicate that oil and gas producing countries and especially the GCC countries face a significant welfare loss risk, due to “unburnable oil” if a worldwide climate regime as recommended by the Paris Agreement is put in place. The development of CDR technologies, in particular direct air capture (DAC) alleviates somewhat this risk and offers these countries a new opportunity for exploiting their gas reserves and the carbon storage capacity offered by depleted oil and gas reservoirs.
Policy iteration for Hamilton–Jacobi–Bellman equations with control constraints
Authors: Kundu Sudeep , Kunisch Karl 
Published: April 2021

AbstractPolicy iteration is a widely used technique to solve the Hamilton Jacobi Bellman (HJB) equation, which arises from nonlinear optimal feedback control theory. Its convergence analysis has attracted much attention in the unconstrained case. Here we analyze the case with control constraints both for the HJB equations which arise in deterministic and in stochastic control cases. The linear equations in each iteration step are solved by an implicit upwind scheme. Numerical examples are conducted to solve the HJB equation with control constraints and comparisons are shown with the unconstrained cases.
ThamizhiMorph: A morphological parser for the Tamil language
Authors: Sarveswaran Kengatharaiyer , Dias Gihan , Butt Miriam 
Published: April 2021

AbstractThis paper presents an open source and extendable Morphological Analyser cum Generator (MAG) for Tamil named ThamizhiMorph. Tamil is a low-resource language in terms of NLP processing tools and applications. In addition, most of the available tools are neither open nor extendable. A morphological analyser is a key resource for the storage and retrieval of morphophonological and morphosyntactic information, especially for morphologically rich languages, and is also useful for developing applications within Machine Translation. This paper describes how ThamizhiMorph is designed using a Finite-State Transducer (FST) and implemented using Foma. We discuss our design decisions based on the peculiarities of Tamil and its nominal and verbal paradigms. We specify a high-level meta-language to efficiently characterise the language’s inflectional morphology. We evaluate ThamizhiMorph using text from a Tamil textbook and the Tamil Universal Dependency treebank version 2.5. The evaluation and error analysis attest a very high performance level, with the identified errors being mostly due to out-of-vocabulary items, which are easily fixable. In order to foster further development, we have made our scripts, the FST models, lexicons, Meta-Morphological rules, lists of generated verbs and nouns, and test data sets freely available for others to use and extend upon.
Spatiotemporal variation of nitrogen and phosphorus and its main influencing factors in Huangshui River basin
Authors: Dong Biqiong , Qin Tianling , Wang Yu , Zhao Yan , Liu Shanshan , Feng Jianming , Li Chenhao , Zhang Xin 
Published: April 2021

AbstractThe foundation of managing excess nutrients in river is the identification of key physical processes and the control of decisive influencing factors. The existing studies seldom consider the influence of rainfall-runoff relationship and only focus on a few anthropogenic activities and natural attributes factors. To address this issue, a comprehensive set of influencing factors including rainfall-runoff relationship (represented by runoff coefficient), basic physical and chemical parameters of water quality, land use types, landscape patterns, topography, and socioeconomic development was constructed in this study. M–K test and cluster analysis were conducted to identify the temporal mutation and spatial clustering characteristics of NH3-N and TP in Huangshui River basin, respectively. Partial least squares regression was used to elucidate the linkages between water contaminants and the factors. As shown in the results, the temporal mutations of NH3-N and TP were obvious in the middle reaches, with 4 out of 7 catchments in the middle reaches have a larger number of mutations of NH3-N than other catchments. The cluster analysis results of NH3-N and TP among catchments were similar. This study also indicated that although the Huangshui River basin was located in the upper reaches of the Yellow River, the influences of rainfall-runoff relationship on spatiotemporal changes of NH3-N and TP in its sub-basins were limited. Only the temporal change of NH3-N in Jintan catchment in the upstream area was significantly affected by runoff coefficient. The indexes of proportion of water area (PWA), proportion of impervious area (PIA), and proportion of primary industry (PPI) were the top three influencing factors of temporal variation of NH3-N and TP for most catchments in the middle reaches. The temporal change of NH3-N in Jintan catchment in the upstream area was obviously affected by runoff coefficient. The spatial variation of NH3-N and TP were all affected by PWA and proportion of secondary industry significantly. The results of this study can provide theoretical basis and technical support for the control and management of nitrogen and phosphorus pollution in upper reaches of rivers.
Cross-age peer tutoring in a technology-enhanced STEAM project at a lower secondary school
Authors: Tenhovirta Satu , Korhonen Tiina , Seitamaa-Hakkarainen Pirita , Hakkarainen Kai 
Published: April 2021

AbstractThe present investigation examined cross-age peer tutoring in the context of organising a technology-enhanced STEAM project aimed at bringing elements of maker culture to a lower secondary school. We examined how 8th graders tutored 7th graders in programming skills. The participants were peer tutors (n = 15) studying in a technology-oriented class, along with their teachers (5) and the researchers (2). By interviewing the tutors, we studied skills that the tutors had experienced as being essential to overcoming the challenges encountered. To trace the tutors’ social support network and the sharing of expertise, we asked the participants to draw a personal social network map. Three key tutors were identified, whose centrality in the network was socially validated by the number of peer tutors seeking their advice. Two case studies of key tutors’ learning networks were carried out. The findings revealed that the tutors needed versatile technological, social, pedagogical, and reflective know-how in the project. It is concluded that cross-age peer tutoring provides significant support for implementing practices of making and STEAM education at school.
Differential Object Marking in Kazakh: The Dynamic Syntax Approach
Authors: Christopher Nadezda 
Published: April 2021

AbstractThis paper presents a novel, Dynamic Syntax-based approach to the phenomenon of differential object marking in Kazakh, which can be extended at least to other Turkic languages displaying this phenomenon. It is demonstrated that the difference in the pragmatics associated with marked and unmarked direct objects, as well as the syntactic restrictions on the positioning of unmarked direct objects, can be elegantly and succinctly explicated through the application of the notions of fixed and unfixed nodes, without the need for proposing special syntactic positions or additional pragmatics for the accusative case.
Electrodeposition of palladium-dotted nickel nanowire networks as a robust self-supported methanol electrooxidation catalyst
Authors: Boettcher Tim , Stojkovikj Sasho , Khadke Prashant , Kunz Ulrike , Mayer Matthew T. , Roth Christina , Ensinger Wolfang , Muench Falk 
Published: April 2021

Abstract
Mass activity and long-term stability are two major issues in current fuel cell catalyst designs. While supported catalysts normally suffer from poor long-term stability but show high mass activity, unsupported catalysts tend to perform better in the first point while showing deficits in the latter one. In this study, a facile synthesis route towards self-supported metallic electrocatalyst nanoarchitectures with both aspects in mind is outlined. This procedure consists of a palladium seeding step of ion track-etched polymer templates followed by a nickel electrodeposition and template dissolution. With this strategy, free-standing nickel nanowire networks which contain palladium nanoparticles only in their outer surface are obtained. These networks are tested in anodic half-cell measurements for demonstrating their capability of oxidising methanol in alkaline electrolytes. The results from the electrochemical experiments show that this new catalyst is more tolerant towards high methanol concentrations (up to $${5}\,\hbox{mol}\,\hbox{L}^{-1}$$

5

mol

L

-
1

) than a commercial carbon supported palladium nanoparticle catalyst and provides a much better long-term stability during potential cycling.

Graphical Abstract
Understanding the farm data lifecycle: collection, use, and impact of farm data on U.S. commercial corn and soybean farms
Authors: Thompson Nathanael M. , DeLay Nathan D. , Mintert James R. 
Published: April 2021

AbstractEnthusiasm regarding the “digital agriculture” revolution is widespread, yet objective research on how commercial farms actually use data and data services remains limited. The purpose of this research is to better understand the current positioning of U.S. commercial corn and soybean farms within the farm data lifecycle, including the collection, use, and impact of farm data. Using survey data from a sample of 800 commercial-scale U.S. corn and soybean farms, the factors associated with progression within the farm data lifecycle are examined. Results indicate that the majority of commercial U.S. corn and soybean farms collect data, indicate that the data they collect influences their decisions, and perceive positive yield benefits as a result of their data-informed decisions. However, farms vary in intensity of their data usage. Investments in data management and analysis resources are associated with progression within the farm data lifecycle. These investments comprise software products that manage and analyze data, including creating GPS maps, layering different data sources, and generating recommendations. Investments in human capital, either in on-farm employees with designated data responsibilities or in trusted off-farm service providers, are also associated with progression within the farm data lifecycle. Farms that have not yet invested in these types of data management and data analysis resources may be forfeiting the potential benefits associated with using their farm’s data to improve on-farm decision making.