bluuebunny/crossref_metadata_2025_split huggingface dataset:
Failure Analysis of Premature Corrosion of HF Seam-Welded Steel Pipe in Central Heating System
Authors: Tyrala Dorota , Pawlowski Bogdan 
Published: March 2021

AbstractPremature corrosion in the form of longitudinal cracking in a high-frequency (HF) induction seam-welded steel pipe occurred after just 24 months in service. The failed pipe was investigated to reveal the main cause of its failure, and the results of microstructural examinations (light optical microscopy, scanning electron microscopy with energy-dispersive spectrometry) suggest that the failure resulted from an HF induction welding process defect—a so-called cast weld, that is, a huge number of iron oxides in the weld line caused by insufficient ejection of the molten metal from the bond line.
One-shot Cluster-Based Approach for the Detection of COVID–19 from Chest X–ray Images
Authors: Aradhya V. N. Manjunath , Mahmud Mufti , Guru D. S. , Agarwal Basant , Kaiser M. Shamim 
Published: March 2021

AbstractCoronavirus disease (COVID-19) has infected over more than 28.3 million people around the globe and killed 913K people worldwide as on 11 September 2020. With this pandemic, to combat the spreading of COVID-19, effective testing methodologies and immediate medical treatments are much required. Chest X-rays are the widely available modalities for immediate diagnosis of COVID-19. Hence, automation of detection of COVID-19 from chest X-ray images using machine learning approaches is of greater demand. A model for detecting COVID-19 from chest X-ray images is proposed in this paper. A novel concept of cluster-based one-shot learning is introduced in this work. The introduced concept has an advantage of learning from a few samples against learning from many samples in case of deep leaning architectures. The proposed model is a multi-class classification model as it classifies images of four classes, viz., pneumonia bacterial, pneumonia virus, normal, and COVID-19. The proposed model is based on ensemble of Generalized Regression Neural Network (GRNN) and Probabilistic Neural Network (PNN) classifiers at decision level. The effectiveness of the proposed model has been demonstrated through extensive experimentation on a publicly available dataset consisting of 306 images. The proposed cluster-based one-shot learning has been found to be more effective on GRNN and PNN ensembled model to distinguish COVID-19 images from that of the other three classes. It has also been experimentally observed that the model has a superior performance over contemporary deep learning architectures. The concept of one-shot cluster-based learning is being first of its kind in literature, expected to open up several new dimensions in the field of machine learning which require further researching for various applications.
Evaluation of Norovirus Reduction in Environmentally Contaminated Pacific Oysters During Laboratory Controlled and Commercial Depuration
Authors: Rupnik Agnieszka , Doré William , Devilly Leon , Fahy James , Fitzpatrick Amy , Schmidt Wiebke , Hunt Kevin , Butler Francis , Keaveney Sinéad 
Published: March 2021

AbstractNorovirus contamination of oysters is the lead cause of non-bacterial gastroenteritis and a significant food safety concern for the oyster industry. Here, norovirus reduction from Pacific oysters (Crassostrea gigas), contaminated in the marine environment, was studied in laboratory depuration trials and in two commercial settings. Norovirus concentrations were measured in oyster digestive tissue before, during and post-depuration using the ISO 15216-1 quantitative real-time RT-PCR method. Results of the laboratory-based studies demonstrate that statistically significant reductions of up to 74% of the initial norovirus GII concentration was achieved after 3 days at 17–21 °C and after 4 days at 11–15 °C, compared to 44% reduction at 7–9 °C. In many trials norovirus GII concentrations were reduced to levels below 100 genome copies per gram (gcg−1; limit of quantitation; LOQ). Virus reduction was also assessed in commercial depuration systems, routinely used by two Irish oyster producers. Up to 68% reduction was recorded for norovirus GI and up to 90% for norovirus GII reducing the geometric mean virus concentration close to or below the LOQ. In both commercial settings there was a significant difference between the levels of reduction of norovirus GI compared to GII (p < 0.05). Additionally, the ability to reduce the norovirus concentration in oysters to < LOQ differed when contaminated with concentrations below and above 1000 gcg−1. These results indicate that depuration, carried out at elevated (> 11 °C) water temperatures for at least 3 days, can reduce the concentration of norovirus in oysters and therefore consumer exposure providing a practical risk management tool for the shellfish industry.
Norovirus Extraction from Frozen Raspberries Using Magnetic Silica Beads
Authors: Raymond Philippe , Paul Sylvianne , Perron André , Deschênes Louise 
Published: March 2021

AbstractHuman noroviruses (HuNoV) are among the main causes of acute gastroenteritis worldwide. Frozen raspberries have been linked to several HuNoV food-related outbreaks. However, the extraction of HuNoV RNA from frozen raspberries remains challenging. Recovery yields are low, and real-time quantitative reverse transcriptase PCR (RT-qPCR) inhibitors limit the sensitivity of the detection methodologies. A new approach using fine magnetic silica beads was developed for the extraction of HuNoV spiked on frozen raspberries. Relatively low recovery yields were observed with both the magnetic silica bead and the reference ISO 15216-1:2017 methods. High RT-qPCR inhibition was observed with the ISO 15216-1:2017 recommended amplification kit but could be reduced by using an alternative kit. Reducing RT-qPCR inhibition is important to limit the number of inconclusive HuNoV assays thus increasing the capacity to assess the HuNoV prevalence in frozen raspberries.
Optimality conditions for nonconvex problems over nearly convex feasible sets
Authors: Ghafari N. , Mohebi H. 
Published: March 2021

AbstractIn this paper, we study the optimization problem (P) of minimizing a convex function over a constraint set with nonconvex constraint functions. We do this by given new characterizations of Robinson’s constraint qualification, which reduces to the combination of generalized Slater’s condition and generalized sharpened nondegeneracy condition for nonconvex programming problems with nearly convex feasible sets at a reference point. Next, using a version of the strong CHIP, we present a constraint qualification which is necessary for optimality of the problem (P). Finally, using new characterizations of Robinson’s constraint qualification, we give necessary and sufficient conditions for optimality of the problem (P).
Fat classes of welded steel details derived from the master design curve of the peak stress method
Authors: Zanetti Michele , Babini Vittorio , Meneghetti Giovanni 
Published: March 2021

AbstractIn this paper, the peak stress method (PSM) is adopted to analyse the fatigue strength of steel welded joints. According to this method, a single design curve is expressed in terms of a properly defined equivalent peak stress and it is valid for fatigue design of arc-welded steel joints. Private companies often need simple finite element beam models for fatigue strength assessments, because of the large dimensions of the structures. However, beam elements provide nominal stresses (and not local stresses) that must be compared with appropriate fatigue strength values (the FAT classes) available in design standards. Due to the limited number of FAT classes available, finding the appropriate one is frequently troublesome, particularly when complex geometries are considered. The objective of this work is to define FAT classes in terms of nominal stress for a number of geometrically complex structural details, starting from the design curve of the PSM. FAT classes have also been determined using the hot spot stress approach. Then the results obtained with the two methods are compared. The structural details analysed in the present paper are typically adopted in amusement park structures and are not classified in common design standards.
Solar force-free magnetic fields
Authors: Wiegelmann Thomas , Sakurai Takashi 
Published: March 2021

AbstractThe structure and dynamics of the solar corona is dominated by the magnetic field. In most areas in the corona magnetic forces are so dominant that all non-magnetic forces such as plasma pressure gradients and gravity can be neglected in the lowest order. This model assumption is called the force-free field assumption, as the Lorentz force vanishes. This can be obtained by either vanishing electric currents (leading to potential fields) or the currents are co-aligned with the magnetic field lines. First we discuss a mathematically simpler approach that the magnetic field and currents are proportional with one global constant, the so-called linear force-free field approximation. In the generic case, however, the relationship between magnetic fields and electric currents is nonlinear and analytic solutions have been only found for special cases, like 1D or 2D configurations. For constructing realistic nonlinear force-free coronal magnetic field models in 3D, sophisticated numerical computations are required and boundary conditions must be obtained from measurements of the magnetic field vector in the solar photosphere. This approach is currently a large area of research, as accurate measurements of the photospheric field are available from ground-based observatories such as the Synoptic Optical Long-term Investigations of the Sun and the Daniel K. Inouye Solar Telescope (DKIST) and space-born, e.g., from Hinode and the Solar Dynamics Observatory. If we can obtain accurate force-free coronal magnetic field models we can calculate the free magnetic energy in the corona, a quantity which is important for the prediction of flares and coronal mass ejections. Knowledge of the 3D structure of magnetic field lines also help us to interpret other coronal observations, e.g., EUV images of the radiating coronal plasma.
Do Targeted Trade Sanctions Against Chinese Technology Companies Affect US Firms? Evidence from an Event Study
Authors: Allen Jeffrey S. 
Published: March 2021

AbstractThis article asks how costly targeted trade sanctions imposed by the US government are for domestic firms. I argue that, as a result of sanctions, the firm value of US companies that have supply relationships with sanctioned entities is likely to suffer from lost revenue, reputational damage, and business model uncertainty. I test this expectation by applying an event study to the important case of targeted trade sanctions against Chinese technology companies. I find that sanctions against these companies reduced their US suppliers’ risk-adjusted stock returns by 220 basis points. Firm-level cross-sectional analysis shows that businesses with stronger ties to the sanctioned entities are more negatively affected, which supports the direct connection between sanctions and relevant suppliers. Measuring the domestic economic ramifications of sanctions for the sender country has been elusive. These findings, which are statistically and economically significant, indicate that US companies face notable costs from sanctions against internationally active firms.
Does Corruption Lead to Lower Subnational Credit Ratings? Fiscal Dependence, Market Reputation, and the Cost of Debt
Authors: Sychowiec Maciej , Bauhr Monika , Charron Nicholas 
Published: March 2021

AbstractWhile studies show a consistent negative relationship between the level of corruption and range indicators of national-level economic performance, including sovereign credit ratings, we know less about the relationship between corruption and subnational credit ratings. This study suggests that federal transfers allow states with higher levels of corruption to retain good credit ratings, despite the negative economic implications of corruption more broadly, which also allows them to continue to borrow at low costs. Using data on corruption conviction in US states and credit ratings between 2001 and 2015, we show that corruption does not directly reduce credit ratings on average. We find, however, heterogeneous effects, in that there is a negative effect of corruption on credit ratings only in states that have a comparatively low level of fiscal dependence on federal transfers. This suggest that while less dependent states are punished by international assessors when seen as more corrupt, corruption does not affect the ratings of states with higher levels of fiscal dependence on federal revenue.
Does the expectation of having to look after parents in the future affect current fertility?
Authors: Sakata Kei , McKenzie C. R. 
Published: March 2021

AbstractThis paper argues that the expectation of having to provide care for aging parents in the future may be a major factor contributing to the current low fertility rate in Japan. Using data from the 1998 and 2008 National Family Research of Japan (NFRJ) surveys and a Poisson-logit hurdle model, this paper examines whether the expectation of having to look after parents in the future affects a couple's current family planning. The first-stage model of a couple's family planning decision is a logit model which examines the decision of whether or not to have any children, and then in the second stage a Poisson model is applied to explain the number of children a couple has conditional on the couple having at least one child. The empirical evidence presented suggests that there are strong generational effects, and that for the post-war cohort, an increase in the probability of having to look after a parent increases the probability of a couple being childless.
Higher rank K-theoretic Donaldson-Thomas Theory of points
Authors: Fasola Nadir , Monavari Sergej , Ricolfi Andrea T. 
Published: March 2021

AbstractWe exploit the critical structure on the Quot scheme$\text {Quot}_{{{\mathbb {A}}}^3}({\mathscr {O}}^{\oplus r}\!,n)$, in particular the associated symmetric obstruction theory, in order to study rankr K-theoreticDonaldson-Thomas (DT) invariants of the local Calabi-Yau$3$-fold${{\mathbb {A}}}^3$. We compute the associated partition function as a plethystic exponential, proving a conjecture proposed in string theory by Awata-Kanno and Benini-Bonelli-Poggi-Tanzini. A crucial step in the proof is the fact, nontrival if$r>1$, that the invariants do not depend on the equivariant parameters of the framing torus$({{\mathbb {C}}}^\ast )^r$. Reducing from K-theoretic tocohomologicalinvariants, we compute the corresponding DT invariants, proving a conjecture of Szabo. Reducing further toenumerativeDT invariants, we solve the higher rank DT theory of a pair$(X,F)$, whereFis an equivariant exceptional locally free sheaf on a projective toric$3$-foldX.As a further refinement of the K-theoretic DT invariants, we formulate a mathematical definition of the chiral elliptic genus studied in physics. This allows us to defineelliptic DT invariantsof${{\mathbb {A}}}^3$in arbitrary rank, which we use to tackle a conjecture of Benini-Bonelli-Poggi-Tanzini.
Bahl Šahastan in the land of the K‘ušans: Medieval Armenian memories of Balkh as an Arsacid capital
Authors: Vacca Alison 
Published: March 2021

AbstractThis paper explores the medieval Armenian understanding of the city of Balkh as a capital of the Arsacid Empire. Medieval Armenian sources employ four strategies of remembrance: scriptural geography, genealogy, folk etymology, and origin stories. These strategies invest the city of Balkh as the source of power of both Armenian royalty and nobility, through their connections to the Great Arsacids. There are two main themes in the descriptions of Balkh. First, the Arsacids of Balkh consistently decimated Sasanian armies in ways that the Armenian Arsacids could not emulate. Second, Balkh emerges as a refuge for (usually Parthian) rebels against the Chinese and Persian Empires. This paper explores the significance of Balkh as a site of memory by placing Armenian constructions of the Great Arsacid past (with some potential echoes of Great Kushan and Kushano-Sasanian history) into dialogue with the history of the city as it appears in Arabic.
Disrupted caregiving behavior as a mediator of the relation between disrupted prenatal maternal representations and toddler social–emotional functioning
Authors: Guyon-Harris Katherine L. , Ahlfs-Dunn Sarah M. , Madigan Sheri , Bronfman Elisa , Benoit Diane , Huth-Bocks Alissa C. 
Published: March 2021

AbstractThe development of maternal representations of the child during pregnancy guides a mother’s thoughts, feelings, and behavior toward her child. The association between prenatal representations, particularly those that are disrupted, and toddler social-emotional functioning is not well understood. The present study examined associations between disrupted prenatal representations and toddler social-emotional functioning and to test disrupted maternal behavior as a mediator of this association. Data were drawn from 109 women from a larger prospective longitudinal study (N=120) of women and their young children. Prenatal disrupted maternal representations were assessed using the Working Model of the Child Interview disrupted coding scheme, while disrupted maternal behavior was coded 12-months postpartum from mother-infant interactions. Mother-reported toddler social-emotional functioning was assessed at ages 12 and 24 months. Disrupted prenatal representations significantly predicted poorer toddler social-emotional functioning at 24 months, controlling for functioning at 12 months. Further, disrupted maternal behavior mediated the relation between disrupted prenatal representations and toddler social-emotional problems. Screening for disrupted representations during pregnancy is needed to facilitate referrals to early intervention and decrease the likelihood of toddler social-emotional problems.
The IFRS 17 contractual service margin: a life insurance perspective
Authors: Yousuf W. , Stansfield J. , Malde K. , Mirin N. , Walton R. , Thorpe B. , Thorpe J. , Iftode C. , Tan L. , Dyble R. , Pelsser A. , Ghosh A. , Qin W. , Berry T. , Er C. 
Published: March 2021

Abstract
IFRS 17 Insurance Contracts is a new accounting standard currently expected to come into force on 1 January 2023. It supersedes IFRS 4 Insurance Contracts. IFRS 17 establishes key principles that entities must apply in all aspects of the accounting of insurance contracts. In doing so, the Standard aims to increase the usefulness, comparability, transparency and quality of financial statements.
A fundamental concept introduced by IFRS 17 is the contractual service margin (CSM). This represents the unearned profit that an entity expects to earn as it provides services. However, as a principles-based standard, IFRS 17 results in entities having to apply significant judgement when determining the inputs, assumptions and techniques it uses to determine the CSM at each reporting period.
In general, the Standard resolves broad categories of mismatches which arise under IFRS 4. Notable examples include mismatches between assets recorded at current market value and liabilities calculated using fixed discount rates as well as inconsistencies in the timing of profit recognition over the duration of an insurance contract. However, there are requirements of IFRS 17 that may create economic or accounting mismatches of its own. For example, new mismatches could arise between the measurement of underlying contracts and the corresponding reinsurance held. Additionally, mismatches can still arise between the measurement of liabilities and the assets that support the liabilities.
This paper explores the technical, operational and commercial issues that arise across these and other areas focusing on the CSM. As a standard that is still very much in its infancy, and for which wider consensus on topics is yet to be achieved, this paper aims to provide readers with a deeper understanding of the issues and opportunities that accompany it.
Extracting information from textual descriptions for actuarial applications
Authors: Manski Scott , Yang Kaixu , Lee Gee Y. , Maiti Tapabrata 
Published: March 2021

AbstractInitial insurance losses are often reported with a textual description of the claim. The claims manager must determine the adequate case reserve for each known claim. In this paper, we present a framework for predicting the amount of loss given a textual description of the claim using a large number of words found in the descriptions. Prior work has focused on classifying insurance claims based on keywords selected by a human expert, whereas in this paper the focus is on loss amount prediction with automatic word selection. In order to transform words into numeric vectors, we use word cosine similarities and word embedding matrices. When we consider all unique words found in the training dataset and impose a generalised additive model to the resulting explanatory variables, the resulting design matrix is high dimensional. For this reason, we use a group lasso penalty to reduce the number of coefficients in the model. The scalable, analytical framework proposed provides for a parsimonious and interpretable model. Finally, we discuss the implications of the analysis, including how the framework may be used by an insurance company and how the interpretation of the covariates can lead to significant policy change. The code can be found in the TAGAM R package (github.com/scottmanski/TAGAM).
The fetal origins of disease: a prospective cohort study on the association of preeclampsia and childhood obesity
Authors: Palma dos Reis Catarina R. , Serrano Fátima , Fonseca Maria João , Martins Ana Teresa , Campos Ana , McMahon Gearoid M. , Barros Henrique 
Published: March 2021

AbstractCardiovascular diseases are the main cause of mortality worldwide, and childhood excess weight/obesity are strong correlators of accumulated risk in later life. A relationship between maternal preeclampsia and offspring’s childhood obesity is recognized, but most studies fail to control for strong confounders. Our goal is to analyze the association between preeclampsia and childhood excess weight/obesity, after accounting for important confounders. We recruited 5133 women with singleton pregnancies during admission for delivery. Sixty-seven pregnancies were complicated by preeclampsia. Maternal and children outcomes were assessed at 10 years of age. We analyzed the association between preeclampsia and childhood excess weight/obesity by fitting a linear regression model (using offspring body mass index (BMI) z-score at 10 years of age) and a logistic regression model (using excess weight/obesity status). We then controlled both models for known confounders, namely maternal prepregnancy BMI, parity, and smoking during pregnancy. At 10 years of age, offspring of preeclamptic mothers had a higher BMI z-score and were more likely classified as overweight/obese, but these differences were not statistically significant. After controlling for maternal prepregnancy BMI, parity, and smoking during pregnancy, there was a high magnitude change in the beta coefficient of preeclampsia in the linear (0.175; −0.014) and the logistic regression models (1.48; 1.23) suggesting that the association between preeclampsia and childhood excess weigh/obesity is significantly confounded by these variables. These confounders also showed a significant association with childhood obesity. This finding suggests that in utero exposure to preeclampsia seems to have less impact in childhood obesity than the previously described confounders.