bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cost-sensitive rainfall thresholds for shallow landslides
Authors: Sala Gianluca , Lanfranconi Camilla , Frattini Paolo , Rusconi Giulia , Crosta Giovanni B. 
Published: June 2021

AbstractThe risk management of rainfall-induced landslides requires reliable rainfall thresholds to issue early warning alerts. The practical application of these thresholds often leads to misclassifications, either false negative or false positive, which induce costs for the society. Since missed-alarm (false negative) and false-alarm (false positive) cost may be significantly different, it is necessary to find an optimal threshold that accounts for and minimises such costs, tuning the false-alarm and missed-alarm rates. In this paper, we propose a new methodology to develop cost-sensitive rainfall thresholds, and we also analyse several factors that produce uncertainty, such as the accuracy of rainfall intensity values at landslide location, the time of occurrence, the minimum rainfall amount to define the non-triggering event, and the variability of cost scenarios. Starting from a detailed mapping of landslides that occurred during five large-scale rainfall events in the Italian Central Alps, we first developed rainfall threshold curves with a ROC-based approach by using both rain gauge and bias-adjusted weather radar data. Then, based on a reference cost scenario in which we quantified several cost items for both missed alarms and false alarms, we developed cost-sensitive rainfall threshold curves by using cost-curve approach (Drummond and Holte 2000). Finally, we studied the sensitivity of cost items. The study confirms how important is the information regarding rainfall intensity at the landslide site for the development of rainfall thresholds. Although the use of bias-corrected radar strongly improves these values, a large uncertainty related to the exact time of landslide occurrence still remains, negatively affecting the analysis. Accounting for the different missed-alarm and false-alarm misclassification costs is important because different combinations of these costs make an increase or decrease of the rainfall thresholds convenient. In our reference cost scenario, the most convenient threshold is lower than ROC-based thresholds because it seeks to minimise the number of missed alarms, whereas the missed-alarm costs are almost seven times greater than false-alarm costs. However, for different cost scenarios, threshold may vary significantly, as much as half an order of magnitude.
Constitutional monarchy as power sharing
Authors: Tridimas George 
Published: June 2021

AbstractIn the transition to democracy some autocracies transformed to republics while others evolved to constitutional monarchies. The paper inquires how constitutional monarchy is established. It models a hereditary king and a liberal challenger who coexist over a succession of periods and fight for power which brings office rents and the right to decide one’s preferred policy. The outcome of the confrontation is uncertain and may vary from period to period. If the king wins, he establishes absolute monarchy, but if the liberal wins he establishes a republic. Instead of fighting they may agree on a constitutional monarchy and share office rents and policy making responsibilities. Whether constitutional monarchy is agreed depends on the marginal utilities from rents and policy preferences of the two actors, the sizes of the benefits from rents and policy, the rates by which they discount the future, and the probabilities of winning office. The contemporary European constitutional monarch as a ceremonial head of state who reigns but does not govern arises as a special case of the general model.
Instabilities at a sheared interface over a liquid laden with soluble surfactant
Authors: Kalogirou A. , Blyth M. G. 
Published: June 2021

AbstractThe linear stability of a semi-infinite fluid undergoing a shearing motion over a fluid layer that is laden with soluble surfactant and that is bounded below by a plane wall is investigated under conditions of Stokes flow. While it is known that this configuration is unstable in the presence of an insoluble surfactant, it is shown via a linear stability analysis that surfactant solubility has a stabilising effect on the flow. As the solubility increases, large-wavelength perturbations are stabilised first, leaving open the possibility of mid-wave instability for moderate surfactant solubilities, and the flow is fully stabilised when the solubility exceeds a threshold value. The predictions of the linear stability analysis are supported by an energy budget analysis which is also used to determine the key physical effects responsible for the (de)stabilisation. Asymptotic expansions performed for long-wavelength perturbations turn out to be non-uniform in the insoluble surfactant limit. In keeping with the findings for insoluble surfactant obtained by Pozrikidis & Hill (IMA J Appl Math 76:859–875, 2011), the presence of the wall is found to be a crucial factor in the instability.
UTLDR: an agent-based framework for modeling infectious diseases and public interventions
Authors: Rossetti Giulio , Milli Letizia , Citraro Salvatore , Morini Virginia 
Published: June 2021

AbstractDue to the SARS-CoV-2 pandemic, epidemic modeling is now experiencing a constantly growing interest from researchers of heterogeneous study fields. Indeed, due to such an increased attention, several software libraries and scientific tools have been developed to ease the access to epidemic modeling. However, only a handful of such resources were designed with the aim of providing a simple proxy for the study of the potential effects of public interventions (e.g., lockdown, testing, contact tracing). In this work, we introduce UTLDR, a framework that, overcoming such limitations, allows to generate “what if” epidemic scenarios incorporating several public interventions (and their combinations). UTLDR is designed to be easy to use and capable to leverage information provided by stratified populations of agents (e.g., age, gender, geographical allocation, and mobility patterns…). Moreover, the proposed framework is generic and not tailored for a specific epidemic phenomena: it aims to provide a qualitative support to understanding the effects of restrictions, rather than produce forecasts/explanation of specific data-driven phenomena.
“The Awesomeness and the Vastness of Who You Really Are:” A Culturally Distinct Framework for Understanding the Link Between Spirituality and Health for Black Sexual Minority Men
Authors: Lassiter Jonathan Mathias , Mims Ivie 
Published: June 2021

AbstractDespite health inequities, many Black sexual minority men are resilient and often utilize spirituality as a culturally distinct self-protective and self-enhancing resource to maintain their health. However, little is known about how spirituality impacts health within a cultural framework that is specific to Black sexual minority men. We conducted 10 individual in-depth interviews, reaching code saturation, with Black sexual minority men across the USA. Our study was guided by grounded theory and a Black psychology theoretical framework. Seven themes were discovered and revealed that participants’ level of spiritual consciousness influenced their engagement in psychological and behavioral processes that were related to mental and physical health. These themes were: (a) suboptimal worldview, (b) emotional revelation, (c) emotional emancipation, (d) emotional regulation, (e) health motivations, (f) health behaviors, and (g) links between spiritual consciousness, mental health, and physical health. Implications of these findings for clinicians and researchers are discussed.
A comparison of statistical relational learning and graph neural networks for aggregate graph queries
Authors: Embar Varun , Srinivasan Sriram , Getoor Lise 
Published: June 2021

AbstractStatistical relational learning (SRL) and graph neural networks (GNNs) are two powerful approaches for learning and inference over graphs. Typically, they are evaluated in terms of simple metrics such as accuracy over individual node labels. Complexaggregate graph queries(AGQ) involving multiple nodes, edges, and labels are common in the graph mining community and are used to estimate important network properties such as social cohesion and influence. While graph mining algorithms support AGQs, they typically do not take into account uncertainty, or when they do, make simplifying assumptions and do not build full probabilistic models. In this paper, we examine the performance of SRL and GNNs on AGQs over graphs with partially observed node labels. We show that, not surprisingly, inferring the unobserved node labels as a first step and then evaluating the queries on the fully observed graph can lead to sub-optimal estimates, and that a better approach is to compute these queries as an expectation under the joint distribution. We propose a sampling framework to tractably compute the expected values of AGQs. Motivated by the analysis of subgroup cohesion in social networks, we propose a suite of AGQs that estimate the community structure in graphs. In our empirical evaluation, we show that by estimating these queries as an expectation, SRL-based approaches yield up to a 50-fold reduction in average error when compared to existing GNN-based approaches.
Migration and fuel use in rural Zambia
Authors: Wu Yu , Entwisle Barbara , Sinai Cyrus , Handa Sudhanshu 
Published: June 2021

AbstractWhat is the effect of migration on fuel use in rural Zambia? Opportunities to increase income can be scarce in this setting; in response, households may pursue a migration strategy to increase resources as well as to mitigate risk. Migrant remittances may make it possible for households to shift from primary reliance on firewood to charcoal, and the loss of productive labor through migration may reinforce this shift. This paper uses four waves of panel data collected as part of the Child Grant Programme in rural Zambia to examine the connection between migration and the choice of firewood or charcoal as cooking fuel and finds evidence for both mechanisms. Importantly, this paper considers migration as a process, including out as well as return migration, embedding it in the context of household dynamics generally. Empirical results suggest that while out-migration helps move households away from firewood as a fuel source, return migration moves them back, but because the former is more common, the overall effect of migration is to shift households away from primary reliance on firewood.
Distributing entanglement with separable states: assessment of encoding and decoding imperfections
Authors: McAleese Hannah , Juska Gediminas , Jahromi Iman Ranjbar , Pelucchi Emanuele , Ferraro Alessandro , Paternostro Mauro 
Published: June 2021

AbstractEntanglement can be distributed using a carrier which is always separable from the rest of the systems involved. Up to now, this effect has predominantly been analyzed in the case where the carrier-system interactions take the form of ideal unitary operations, thus leaving untested its robustness against either non-unitary or unitary errors. We address this issue by considering the effect of incoherent dynamics acting alongside imperfect unitary interactions. In particular, we determine the restrictions that need to be placed on the interaction time, as well as the strength of the incoherent dynamics. We find that with non-unitary errors, we can still successfully distribute entanglement, provided we measure the carrier in a suitable basis. Introducing imperfections in the unitary dynamics, we show that entanglement gain is possible even with substantial unitary errors. Moreover, certain variations in the strength of the unitary dynamics can allow for greater robustness against non-unitary errors. Therefore, even in experimental settings where unitary operations cannot be carried out without imperfections, it is still possible to generate entanglement between two systems using a separable carrier.
Serum metabolic fingerprinting of psoriasis and psoriatic arthritis patients using solid-phase microextraction—liquid chromatography—high-resolution mass spectrometry
Authors: Looby Nikita , Roszkowska Anna , Reyes-Garcés Nathaly , Yu Miao , Bączek Tomasz , Kulasingam Vathany , Pawliszyn Janusz , Chandran Vinod 
Published: June 2021

Abstract
Introduction
Psoriatic arthritis (PsA), an inflammatory arthritis that develops in individuals with psoriasis, is associated with reduced quality of life. Identifying biomarkers associated with development of PsA as well as with PsA disease activity may help management of psoriatic disease.

Objectives
To use metabolomic fingerprinting to determine potential candidate markers of disease conversion (psoriasis to PsA) and/or PsA activity.

Methods
A novel sample preparation protocol based on solid-phase microextraction (SPME) was used to prepare serum samples obtained from: (1) individuals with psoriasis, some of whom develop psoriatic arthritis (n = 20); (2) individuals with varying PsA activity (mild, moderate, severe; n = 10 each) and (3) healthy controls (n = 10). Metabolomic fingerprinting of the obtained extracts was performed using reversed-phase liquid chromatography coupled to high resolution mass spectrometry.

Results
Psoriasis patients who developed PsA had similar metabolomic profiles to patients with mild PsA and were also indistinguishable from patients with psoriasis who did not develop PsA. Elevated levels of selected long-chain fatty acids (e.g., 3-hydroxytetradecanedioic acid) that are associated with dysregulation of fatty acid metabolism, were observed in patients with severe PsA. In addition, 1,11-undecanedicarboxylic acid—an unusual fatty acid associated with peroxisomal disorders—was also identified as a classifier in PsA patients vs. healthy individuals. Furthermore, a number of different eicosanoids with either pro- or anti-inflammatory properties were detected solely in serum samples of patients with moderate and severe PsA.

Conclusion
A global metabolomics approach was employed to analyze the serum metabolome of patients with psoriasis, PsA, and healthy controls in order to examine potential differences in the biochemical profiles at a metabolite level. A closer examination of circulating metabolites may potentially provide markers of PsA activity.
Influence of High-Productivity Process Parameters on the Surface Quality and Residual Stress State of AISI 316L Components Produced by Directed Energy Deposition
Authors: Piscopo Gabriele , Salmi Alessandro , Atzeni Eleonora 
Published: June 2021

AbstractThe production of large components is one of the most powerful applications of laser powder-directed energy deposition (LP-DED) processes. High productivity could be achieved, when focusing on industrial applications, by selecting the proper process parameters. However, it is of crucial importance to understand the strategies that are necessary to increase productivity while maintaining the overall part quality and minimizing the need for post-processing. In this paper, an analysis of the dimensional deviations, surface roughness and subsurface residual stresses of samples produced by LP-DED is described as a function of the applied energy input. The aim of this work is to analyze the effects of high-productivity process parameters on the surface quality and the mechanical characteristics of the samples. The obtained results show that the analyzed process parameters affect the dimensional deviations and the residual stresses, but have a very little influence on surface roughness, which is instead dominated by the presence of unmelted particles.
Seed germination behaviour of the narrow endemic Daphne arbuscula (Thymelaeaceae) compared to the more widespread Daphne cneorum
Authors: Di Sacco Alice , Gajdošová Zuzana , Slovák Marek , Turisová Ingrid , Turis Peter , Kučera Jaromír , Müller Jonas V 
Published: June 2021

AbstractDiminished reproduction success in species with narrow distribution ranges might be one of the factors responsible for their limited dispersal and colonization abilities. We investigated here various aspects of the seed biology of the West Carpathian endemic Daphne arbuscula (Thymelaeaceae) and compared it with its more widespread relative D. cneorum. In both species, we investigated (i) differences in seed viability and germination ability; (ii) differences between the two observed fruit morphotype groups, and (iii) the effect of cold stratification in breaking seed dormancy and enhance germination in stored seeds. To determine seed viability, a tetrazolium test and an imbibed cut test were performed. Several seed germination tests with gibberellic acid and with a sequence of cold and warm stratification, using different temperatures and durations, were carried out. We uncovered that (i) D. arbuscula seeds show significantly lower viability than D. cneorum seeds, but this difference is due to the smaller-fruit morphotype; (ii) seed quality and viability of the big-fruit morphotype are significantly greater than the smaller-fruit morphotype in both species, although the seed viability of the latter is not null and the dormancy level seems to differ between them; (iii) a warm stratification at 15°C for 13 weeks, followed by cold stratification at either 0 or 5°C for 28 weeks, followed by 4 weeks at 15°C, break physiological dormancy and allow the majority of seeds of D. arbuscula (63%) to germinate. We recommend including both fruit morphotypes when collecting seed of Daphne for ex situ conservation and reintroduction initiatives, to maintain the original genetic diversity of the species.
A novel automated tower graph based ECG signal classification method with hexadecimal local adaptive binary pattern and deep learning
Authors: Subasi Abdulhamit , Dogan Sengul , Tuncer Turker 
Published: June 2021

AbstractElectrocardiography (ECG) signal recognition is one of the popular research topics for machine learning. In this paper, a novel transformation called tower graph transformation is proposed to classify ECG signals with high accuracy rates. It employs a tower graph, which uses minimum, maximum and average pooling methods altogether to generate novel signals for the feature extraction. In order to extract meaningful features, we presented a novel one-dimensional hexadecimal pattern. To select distinctive and informative features, an iterative ReliefF and Neighborhood Component Analysis (NCA) based feature selection is utilized. By using these methods, a novel ECG signal classification approach is presented. In the preprocessing phase, tower graph-based pooling transformation is applied to each signal. The proposed one-dimensional hexadecimal adaptive pattern extracts 1536 features from each node of the tower graph. The extracted features are fused and 15,360 features are obtained and the most discriminative 142 features are selected by the ReliefF and iterative NCA (RFINCA) feature selection approach. These selected features are used as an input to the artificial neural network and deep neural network and 95.70% and 97.10% classification accuracy was obtained respectively. These results demonstrated the success of the proposed tower graph-based method.
Improving drilling hydraulics estimations-a case study
Authors: Ashena Rahman , Hekmatinia Abdol-Azim , Ghalambor Ali , Aadnoy Bernt , Enget Charissa , Rasouli Vamegh 
Published: June 2021

AbstractAccurate pressure drop estimation is important for drill string and bit nozzles design and optimized fluid circulations as well as identifying the drilling problems such as bit nozzle(s) washout or plugging. In this study, the Bingham Plastic model has been modified by applying a coefficient to its turbulent pressure loss calculations. This coefficient encompasses the effects of the drill pipe tool joints and other effects in estimation of pressure losses. The range of the coefficient was determined in field applications for different hole sizes and mud types. The results showed that applying a correction coefficient of 1.08–1.12 to turbulent pressure loss equations (depending on borehole size and mud type) improves the pressure loss estimation. By applying this coefficient, the estimated pressure losses are increased to compensate the under-estimation of the Bingham Plastic model. This is considered a significant contribution to accurate calculation of borehole hydraulics and in-time detection and identification of borehole problems and reduction of invisible lost time. The findings also showed that this enhanced effect is independent of the mud type. The use of this coefficient removes the necessity of using rather complex mud rheological models such as the Herschel–Bulkley model.
Cloisite 10A as an Effective Antibacterial Agent in Polymer Matrices: Role of Nanoscale Roughness and Interfacial Interactions
Authors: Snigdha S. , Nandakumar K. , Sabu T. , Radhakrishnan E. K. 
Published: June 2021

AbstractPolymer–filler interactions play a major role in determining the antibacterial activity of organoclay in nanocomposites. The objective of the current study was to determine the effect of polymer type on the antibacterial properties of an organically modified clay – cloisite 10A (C10A) – using poly-ε-caprolactone (PCL) and poly-L-lactic acid (PLA) polymeric systems. Nanocomposite characterization using atomic force microscopy (AFM) showed an increase in roughness upon addition of the clay mineral, and X-ray diffraction (XRD) showed intercalation of the selected polymers into the interlayer spaces of the clay. Transmission electron microscopy (TEM) analysis supported the XRD findings. C10A in PCL thin films enhanced the bactericidal activity against Staphylococcus aureus when compared to the C10A in PLA. The observed change could be the result of pronounced levels of interaction between the filler and polymer matrix in the C10A-PLA nanocomposite when compared to C10A-PCL. The higher interaction levels could hinder the diffusion of bactericidal agents from the nanocomposite membranes. The present study provided insight into the nature of interaction between nanocomposite components and its impact on bioactivity, which can have applications in terms of generating engineered antibacterial materials.
Deconstructing the SolarWinds breach
Authors: Lazarovitz Lavi 
Published: June 2021

The now infamous SolarWinds breach is still causing ramifications for the businesses involved. It won't be long until we see another attack of this kind, because most organisations currently don't have the ability to quickly find and respond to attackers that have infiltrated their networks. Lavi Lazarovitz of CyberArk Labs dissects the SolarWinds case, examining the major steps used by the attackers. And he makes recommendations for how organisations can avoid falling victim to this form of attack in the future. In 2021 we’ve already seen a number of far-reaching cyber attacks. The now infamous SolarWinds breach is still causing ramifications for the businesses involved, and the recent hack on Microsoft exchange servers has reportedly put more than 3,000 UK email servers at risk. 1