bluuebunny/crossref_metadata_2025_split huggingface dataset:
Trying to Serve Two Masters is Easy, Compared to Three: Identity Multiplicity Work by Christian Impact Investors
Authors: Smith Brett R. , Lawson Amanda , Jones Jessica , Holcomb Tim , Minnich Aimee 
Published: June 2022

AbstractWhile research has focused on financial and social goals in impact investing, we add to the limited research that focuses on how individuals manage identity multiplicity, defined as three or more role identities. Based on our qualitative study of Christian impact investors, we develop a model of identity multiplicity work, explaining how individuals manage their multiple role identities (financial, social, and religious) to reduce identity tensions during the process of impact investing. We find individuals engaged in an interactive, ongoing three-step process of identity multiplicity work: prioritizing one of their salient identities, managing their identity multiplicity interrelationships, and reinforcing their prioritized identity. Investors generally prioritized an identity that was neither financial nor social, but rather religious. We also find this identity work implemented through three novel mechanisms: shadowing, one identity casts a shadow over another thereby enabling the simultaneous pursuit of related goals; distinguishing, all identities are retained and at least a minimum threshold of role expectations are met; and surrendering, partial sacrificing of goals of one (or more) identity in favor of another identity based on an individual’s self-reflective importance of the role. Our findings offer new insights to multiple identities, impact investing and business ethics literatures.
Fast and parallel decomposition of constraint satisfaction problems
Authors: Gottlob Georg , Okulmus Cem , Pichler Reinhard 
Published: June 2022

AbstractConstraint Satisfaction Problems (CSP) are notoriously hard. Consequently, powerful decomposition methods have been developed to overcome this complexity. However, this poses the challenge of actually computing such a decomposition for a given CSP instance, and previous algorithms have shown their limitations in doing so. In this paper, we present a number of key algorithmic improvements and parallelisation techniques to compute so-called Generalized Hypertree Decompositions (GHDs) faster. We thus advance the ability to compute optimal (i.e., minimal-width) GHDs for a significantly wider range of CSP instances on modern machines. This lays the foundation for more systems and applications in evaluating CSPs and related problems (such as Conjunctive Query answering) based on their structural properties.
Improving Zinc and Iron Biofortification in Wheat through Genomics Approaches
Authors: Wani Shabir Hussain , Gaikwad Kiran , Razzaq Ali , Samantara Kajal , Kumar Manjeet , Govindan Velu 
Published: June 2022

AbstractGlobally, about 20% of calories (energy) come from wheat. In some countries, it is more than 70%. More than 2 billion people are at risk for zinc deficiency and even more, people are at risk of iron deficiency, nearly a quarter of all children underage group of 5 are physically and cognitively stunted, and lack of dietary zinc is a major contributing factor. Biofortified wheat with elevated levels of zinc and iron has several potential advantages as a delivery vehicle for micronutrients in the diets of resource-poor consumers who depend on cereal-based diets. The conventional breeding strategies have been successful in the introduction of novel alleles for grain Zn and Fe that led to the release of competitive Zn enriched wheat varieties in South Asia. The major challenge over the next few decades will be to maintain the rates of genetic gains for grain yield along with increased grain Zn/Fe concentration to meet the food and nutritional security challenges. Therefore, to remain competitive, the performance of Zn-enhanced lines/varieties must be equal or superior to that of current non-biofortified elite lines/varieties. Since both yield and Zn content are invisible and quantitatively inherited traits except few intermediate effect QTL regions identified for grain Zn, increased breeding efforts and new approaches are required to combine them at high frequency, ensuring that Zn levels are steadily increased to the required levels across the breeding pipelines. The current review article provides a comprehensive list of genomic regions for enhancing grain Zn and Fe concentrations in wheat including key candidate gene families such NAS, ZIP, VLT, ZIFL, and YSL. Implementing forward breeding by taking advantage of the rapid cycling trait pipeline approaches would simultaneously introgress high Zn and Fe QTL into the high Zn and normal elite lines, further increasing Zn and Fe concentrations.
Panel data and descriptor for energy econometrics – an efficiency, resilience and innovation analysis
Authors: Aldieri Luigi , Gatto Andrea , Vinci Concetto Paolo 
Published: June 2022

AbstractThe work at hand presents a new extensive panel dataset for energy economics, econometrics and policy. The referred dataset is made of 5000 observations circa, including 6 energy economics variables and the majority of the world’s countries (n = 136), extended for 6 years (2009–2014). Data can be used for diverse energy econometrics studies, especially for socioeconomic and environmental aspects of energy innovation and efficiency investigations. The analysed data can be exploited for further analyses to improve our understanding of resilience and vulnerability aspects of the domestic industry, examined at the global scale. To this end, several databases were chosen from the IEA, the World Bank and their partners. Data were collected, cleaned, treated, harmonised and analysed to return a new panel dataset. Both the new data organisation and the descriptor can be used as tools and guidance to perform sustainability, innovation and entrepreneurial inquiries and analyses, focusing on energy economics, econometrics and development policy.
Mechanical and biodeterioration behaviours of a clayey soil strengthened with combined carrageenan and casein
Authors: Ni Jing , Li Shan-Shan , Geng Xue-Yu 
Published: June 2022

AbstractIn the last decade, biopolymers have been used as organic soil binders in ground improvement and earthen construction material modification. Although biopolymer-treated soils have substantially enhanced mechanical strength, the deformation characteristics under external loads and material durability (e.g. biodeterioration due to microbial activity) have not yet been fully understood, which limits the in situ practical application of the biopolymer-based soil treatment technology. This study investigated the efficiency of combined carrageenan and casein in strengthening a clayey soil with the biodeterioration consideration. Both mechanical tests (e.g. unconfined compressive strength and one-dimensional consolidation) and biological tests (e.g. high throughput sequencing and rating of mould growth) were conducted. Results indicated that the usage of the carrageenan–casein mixture induced a higher soil compressive strength compared with either carrageen or casein, due to the formation of a three-dimensional gel network. In addition, carrageenan–casein mixture and casein decreased the compressibility of the clayey soil, which might be attributed to the casein’s peculiarity of self-associating into micelles, leading to minimal interactions with water molecules. Carrageenan, due to its affinity for water, increased the soil compressibility. Under the impact of microbial activity, the biopolymer-treated soils underwent deterioration in both surface appearance (i.e. coloured stains and patches caused by mould growth) and compressive strength. A linear relationship was proposed, in which a reduction in compressive strength by approximately 11% is expected while the rating of mould growth is increased by one in a five-rating system. The current research demonstrates that the soil reinforcement with combined carrageenan and casein is able to improve both soil strength and deformation behaviours. It is also suggested to take into account the biodeterioration considerations in the design and implementation of biopolymer-based soil reinforcement practices.
Automated segmentation of head CT scans for computer-assisted craniomaxillofacial surgery applying a hierarchical patch-based stack of convolutional neural networks
Authors: Steybe David , Poxleitner Philipp , Metzger Marc Christian , Brandenburg Leonard Simon , Schmelzeisen Rainer , Bamberg Fabian , Tran Phuong Hien , Kellner Elias , Reisert Marco , Russe Maximilian Frederik 
Published: June 2022

Abstract
Purpose
Computer-assisted techniques play an important role in craniomaxillofacial surgery. As segmentation of three-dimensional medical imaging represents a cornerstone for these procedures, the present study was aiming at investigating a deep learning approach for automated segmentation of head CT scans.

Methods
The deep learning approach of this study was based on the patchwork toolbox, using a multiscale stack of 3D convolutional neural networks. The images were split into nested patches using a fixed 3D matrix size with decreasing physical size in a pyramid format of four scale depths. Manual segmentation of 18 craniomaxillofacial structures was performed in 20 CT scans, of which 15 were used for the training of the deep learning network and five were used for validation of the results of automated segmentation. Segmentation accuracy was evaluated by Dice similarity coefficient (DSC), surface DSC, 95% Hausdorff distance (95HD) and average symmetric surface distance (ASSD).

Results
Mean for DSC was 0.81 ± 0.13 (range: 0.61 [mental foramen] – 0.98 [mandible]). Mean Surface DSC was 0.94 ± 0.06 (range: 0.87 [mental foramen] – 0.99 [mandible]), with values > 0.9 for all structures but the mental foramen. Mean 95HD was 1.93 ± 2.05 mm (range: 1.00 [mandible] – 4.12 mm [maxillary sinus]) and for ASSD, a mean of 0.42 ± 0.44 mm (range: 0.09 [mandible] – 1.19 mm [mental foramen]) was found, with values < 1 mm for all structures but the mental foramen.

Conclusion
In this study, high accuracy of automated segmentation of a variety of craniomaxillofacial structures could be demonstrated, suggesting this approach to be suitable for the incorporation into a computer-assisted craniomaxillofacial surgery workflow. The small amount of training data required and the flexibility of an open source-based network architecture enable a broad variety of clinical and research applications.
Somatic Mutations and Clonal Hematopoiesis as Drivers of Age-Related Cardiovascular Risk
Authors: Haring Bernhard , Wissel Stephanie , Manson JoAnn E. 
Published: June 2022

Abstract
Purpose of Review
Clonal hematopoiesis of indeterminate potential (CHIP) has been identified as a novel cardiovascular risk factor. Here we review the relationship of lifestyle and environmental risk factors predisposing to somatic mutations and CHIP and provide an overview on age-related cardiovascular outcomes.

Recent Findings
CHIP has been associated with accelerated atherosclerosis and cardiovascular disease in both epidemiological and experimental studies. The most commonly mutated candidate driver genes are DNMT3A, TET2, JAK2, and ASXL1. The underlying mechanisms appear predominantly related to inflammatory pathways. Although age is the dominant risk factor for developing CHIP, emerging evidence suggests that other factors such as smoking, obesity/type 2 diabetes, or an unhealthy diet play a role in the occurrence of somatic mutations.

Summary
Evidence suggests a strong link between vascular risk factors, somatic hematopoietic mutations, and age-related cardiovascular disease. Further studies on CHIP biology are required to identify targeted interventions for risk reduction in patients with CHIP and inform the utility of screening strategies.
Epidemiology and outcomes of primary sclerosing cholangitis: an Australian multicentre retrospective cohort study
Authors: Tan Natassia , Ngu N. , Worland T. , Lee T. , Abrahams T. , Pandya K. , Freeman E. , Hannah N. , Gazelakis K. , Madden R. G. , Lynch K. D. , Valaydon Z. , Sood S. , Dev A. , Bell S. , Thompson A. , Ding J. , Nicoll A. J. , Liu K. , Gow P. , Lubel J. , Kemp W. , Roberts S. K. , Majeed A. ,   the Melbourne Liver Group
Published: June 2022

Abstract
Background and aims
Little is known regarding the epidemiology and outcomes of patients with primary sclerosing cholangitis (PSC) in Australia. We, therefore, evaluated the epidemiology and clinical outcomes of PSC in a large cohort of Australian patients and compared these to the general population.

Methods
We conducted a multicentre, retrospective cohort study of PSC patients at nine tertiary liver centers across three Australian states, including two liver transplant centers.

Results
A total of 413 PSC patients with 3,285 person-years of follow-up were included. Three hundred and seventy-one (90%) patients had large duct PSC and 294 (71%) had associated inflammatory bowel disease. A total of 168 (41%) patients developed cirrhosis (including 34 at the time of PSC diagnosis) after a median of 15.8 (95% CI 12.4, NA) years. The composite endpoint of death or liver transplantation occurred in 49 (12%) and 78 (19%) patients, respectively, with a median transplant-free survival of 13.4 (95% CI 12.2–15) years. Compared to the general population, PSC accounted for a 240-fold increased risk of development of cholangiocarcinoma (CCA) and CCA-related death. CCA risk was increased with older age of PSC diagnosis, presence of dominant stricture and colectomy. Compared to same-aged counterparts in the general population, PSC patients who were diagnosed at an older age or with longer disease duration had reduced relative survival.

Conclusion
In this large retrospective cohort study of PSC patients in Australia, increased age and time from diagnosis was associated with increased mortality and morbidity particularly from CCA and development of cirrhosis, necessitating need for liver transplant.
Automated machine learning pipeline for geochemical analysis
Authors: Alférez Germán H. , Esteban Oscar A. , Clausen Benjamin L. , Ardila Ana María Martínez 
Published: June 2022

AbstractBiplot diagrams are traditionally used for rock discrimination using geochemical data from samples. However, this approach has limitations when facing a high number of variables. Machine learning has been proposed as an alternative to analyze multivariate data for more than 70 years. However, the application of machine learning by geoscientists is still complicated since there are no tools that propose a pipeline that can be followed from preparing the data to evaluating the models. Automated machine learning aims to face this issue by automating the creation and evaluation of machine learning models. The contribution of this work is twofold. First, we propose a methodology that follows a pipeline for the application of supervised and unsupervised learning to geochemical data. Both methods were applied to a dataset of granitic rock samples from 6 blocks in the Peninsular Ranges and the Transverse Ranges Provinces in Southern California. For supervised learning, the Decision Trees model offered the best values to classify the samples from this region: accuracy: 87%; precision: 89%; recall: 89%; and F-score: 81%. For unsupervised learning, 2 components were related to pressure effects, and another 2 could be related to water effects. As a second contribution, we propose a web application that follows the proposed methodology to analyze geochemical data using automated machine learning. It allows data preparation using techniques such as imputation and upsampling, the application of supervised and unsupervised learning, and the evaluation of the models. All this without the need to program.
The most salient global predictors of adolescents’ subjective Well-Being: parental support, peer support, and anxiety
Authors: Wu Yi-Jhen , Lee Jihyun 
Published: June 2022

AbstractThe present study examined the globally relevant predictors of adolescents’ subjective well-being (SWB) using data from the Programme for International Student Assessment (PISA) 2015 Student Questionnaire. Guided by the social-cognitive-behavior model (Suldo et al., 2008) and self-determination theory (Ryan & Deci, 2000), nine variables were selected and subjected to a statistical procedure of decision tree modeling (DTM). Our results showed that parent and peer support were the most salient predictors of adolescents’ SWB across all eight world regions under investigation, followed by anxiety and gender. However, we also noted some variations in the relative importance of parent and peer support and multiple pathways that lead to SWB. The study concludes with practical implications.
WaSim model for subsurface drainage design using soil hydraulic parameters estimated by pedotransfer functions
Authors: Malota Mphatso , Mchenga Joshua , Chunga Brighton Austin 
Published: June 2022

AbstractThe agricultural drainage engineering community is steadily shifting the design of subsurface drainage systems from the experience-based design approach to the simulation-based design approach. As with any design problem, two challenges are faced; firstly, how to determine all the input data required by the simulation model, and secondly to, a priori, anticipate what the performance of the designed system will be. This study sought to evaluate the performance of the WaSim model to simulate fluctuating water table depths (WTD), and drainage discharges (DD) in KwaZulu-Natal Province, South Africa. Saturated hydraulic conductivity (Ksat), which is an input to the WaSim model, was estimated by the Rosetta computer program, based on soil particle size distribution data, bulk density, and soil water retention characteristics at pressure heads of – 33 and – 1500 kPa. performance of the WaSim model was statistically assessed using the coefficient of determination (R2), coefficient of residual mass (CRM), mean absolute error (MAE), mean percent error (MPE), and the nash–sutcliffe efficiency (NSE). during the validation period, the WaSim model predicted WTDs with R2, CRM, MAE, MPE, and NSE of 0.86, 0.003, 4.9 cm, 6.0%, and 0.98, respectively. In the same validation period, the model predicted DDs with R2, CRM, MAE, MPE, and NSE of 0.57, 0.002, 0.30 mm day−1,11%, and 0.76, respectively. These results suggest that the use of Rosetta-estimated Ksat data as inputs to the WaSim model compromised its accuracy and applicability as a subsurface drainage design tool. Owing to the relatively low R2 value of 0.57, and that the WaSim model was empirically developed, we recommend further improvement on the calibration of the model for it to be suitable for application under the prevailing conditions. Also, in the absence of other means of determining Ksat, we caution the use of Rosetta-estimated Ksat data as inputs to the WaSim model for the design and analysis of subsurface drainage systems in KwaZulu-Natal Province, South Africa.
The Struggle for AI’s Recognition: Understanding the Normative Implications of Gender Bias in AI with Honneth’s Theory of Recognition
Authors: Waelen Rosalie , Wieczorek Michał 
Published: June 2022

Abstract
AI systems have often been found to contain gender biases. As a result of these gender biases, AI routinely fails to adequately recognize the needs, rights, and accomplishments of women. In this article, we use Axel Honneth’s theory of recognition to argue that AI’s gender biases are not only an ethical problem because they can lead to discrimination, but also because they resemble forms of misrecognition that can hurt women’s self-development and self-worth. Furthermore, we argue that Honneth’s theory of recognition offers a fruitful framework for improving our understanding of the psychological and normative implications of gender bias in modern technologies. Moreover, our Honnethian analysis of gender bias in AI shows that the goal of responsible AI requires us to address these issues not only through technical interventions, but also through a change in how we grant and deny recognition to each other.
Generic identifiability of pairs of ternary forms
Authors: Beorchia Valentina , Galuppi Francesco 
Published: June 2022

AbstractWe prove that two general ternary forms of degrees $$c\le d$$

c
≤
d

are simultaneously identifiable only in the classical cases $$(c,d)=(2,2)$$

(
c
,
d
)
=
(
2
,
2
)

and $$(c,d)=(2,3)$$

(
c
,
d
)
=
(
2
,
3
)

. We translate the problem into the study of a certain linear system on a projective bundle on the plane, and we apply techniques from projective and birational geometry to prove that the associated map is not birational.
Mortality in cancer patients with SARS-CoV-2 or seasonal influenza: an observational cohort study from a German-wide hospital network
Authors: Kodde Cathrin , Bonsignore Marzia , Schöndube Daniel , Bauer Torsten , Hohenstein Sven , Bollmann Andreas , Meier-Hellmann Andreas , Kuhlen Ralf , Nachtigall Irit 
Published: June 2022

Abstract
Purpose
At the beginning of the COVID-19 pandemic, SARS-CoV-2 was often compared to seasonal influenza. We aimed to compare the outcome of hospitalized patients with cancer infected by SARS-CoV-2 or seasonal influenza including intensive care unit admission, mechanical ventilation and in-hospital mortality.

Methods
We analyzed claims data of patients with a lab-confirmed SARS-CoV-2 or seasonal influenza infection admitted to one of 85 hospitals of a German-wide hospital network between January 2016 and August 2021.

Results
29,284 patients with COVID-19 and 7442 patients with seasonal influenza were included.
Of these, 360 patients with seasonal influenza and 1625 patients with COVID-19 had any kind of cancer. Cancer patients with COVID-19 were more likely to be admitted to the intensive care unit than cancer patients with seasonal influenza (29.4% vs 24.7%; OR 1.31, 95% CI 1.00–1.73 p < .05). No statistical significance was observed in the mechanical ventilation rate for cancer patients with COVID-19 compared to those with seasonal influenza (17.2% vs 13.6% OR 1.34, 95% CI 0.96–1.86 p = .09). 34.9% of cancer patients with COVID-19 and 17.9% with seasonal influenza died (OR 2.45, 95% CI 1.81–3.32 p < .01). Risk factors among cancer patients with COVID-19 or seasonal influenza for in-hospital mortality included the male gender, age, a higher Elixhauser comorbidity index and metastatic cancer.

Conclusion
Among cancer patients, SARS-CoV-2 was associated with a higher risk for in-hospital mortality than seasonal influenza. These findings underline the need of protective measurements to prevent an infection with either COVID-19 or seasonal influenza, especially in this high-risk population.
Statistical applications of contrastive learning
Authors: Gutmann Michael U. , Kleinegesse Steven , Rhodes Benjamin 
Published: June 2022

AbstractThe likelihood function plays a crucial role in statistical inference and experimental design. However, it is computationally intractable for several important classes of statistical models, including energy-based models and simulator-based models. Contrastive learning is an intuitive and computationally feasible alternative to likelihood-based learning. We here first provide an introduction to contrastive learning and then show how we can use it to derive methods for diverse statistical problems, namely parameter estimation for energy-based models, Bayesian inference for simulator-based models, as well as experimental design.