bluuebunny/crossref_metadata_2025_split huggingface dataset:
Near-source ground motion estimation for assessing the seismic hazard of critical facilities in central Italy
Authors: Moratto Luca , Santulin Marco , Tamaro Alberto , Saraò Angela , Vuan Alessandro , Rebez Alessandro 
Published: November 2022

AbstractWe apply the Probabilistic Seismic Hazard Analysis (PSHA) and compute Physics-Based Simulations (PBS) of ground motion for three dams in the Campotosto area (Central Italy). The dams, which confine an artificial water reservoir feeding hydroelectric power plants, are located in an active seismic zone between the areas that experienced the 2009 L’Aquila and 2016–2017 Central Italy seismic sequences. The probabilistic disaggregation estimated for a return period of 2475 years, corresponding to the collapse limit state for critical facilities, indicates that the most dangerous fault is associated with a maximum magnitude of 6.75 ± 0.25 within a distance of 10 km. This fault is used in PBS to emulate the Maximum Credible Earthquake scenario. To capture the ground motion variability, we input a pseudo-dynamic source model to encompass spatial and temporal variations in the slip, rise time and rupture propagation, heavily affecting the near-source ground motion. Indeed, the ground motion above the rupture volume is mainly influenced by the epistemic uncertainties of rupture nucleation and slip distribution. The computed broadband seismograms are consistent with the near-source shaking recorded during the 2016 MW6.6 Norcia earthquake and constrain the upper bound of the simulated ground motion at specific sites. Our modelling reinforces the importance of considering vertical ground motion near the source in seismic design. It could reach shaking values comparable to or larger than those of the horizontal components. This approach can be applied in other areas with high seismic hazard to evaluate the seismic safety of existing critical facilities.
A ‘Lens of Labor’: Re-Conceptualizing Young People’s Involvement in Organized Crime
Authors: Atkinson-Sheppard Sally 
Published: November 2022

AbstractMillions of the world’s children engage in labor, often exploitative and essential to their survival. Child labor is closely related to crime; global discourse illustrates how young people are victims of forced and bonded labor and recent studies from the global South demonstrate how young people are hired as the ‘illicit laborers’ of organized crime groups. Despite this, there is a tendency to consider young people, not as laborers but as victims of trafficking or as offenders (often in relation to gangs). To address this lacuna, the article draws on data from 3 studies conducted in the global South to develop a conceptual framework suitable for understanding the intersection between labor and crime. The article develops a metaphorical ‘labor lens’, a lens which centers and prioritizes labor and instrumental drivers for crime, embedded within wider structures of illicit markets, established organized crime, state:crime collaboration and the need for children to work to survive. The article integrates economic drivers for involvement in organized crime with the moral economy, within the context of ecological frameworks of crime, embedded with wider issues of coloniality. In doing so, the article develops a new conceptual framework for considering young people’s involvement in organized crime.
Factors affecting the Acceptance of Video Games as a Tool to improve students’ academic performance in Physical Education
Authors: Merino-Campos Carlos , del-Castillo Héctor , Medina-Merodio José-Amelio 
Published: November 2022

AbstractUsing commercial videogames as learning objects for the development of 21st century skills in secondary schools has been a relevant topic of study in recent years. But its use in the field of physical education is especially interesting considering its nature. The aim of this article is to explore how elements like technological competence, game-based learning, accessibility, and enjoyment are relevant factors that influence the acceptance of the use of video games as a learning aid in the context of physical education, as well as their influence on academic performance in the subject itself. The study enlisted the participation of 166 secondary school students. A quasi-experimental pre-post design with a non-equivalent control group was used. The intervention consisted of a specific training program entailing nine one-hour sessions once a week. To develop the model, the data was analysed using the optimisation technique of partial least squares (PLS). Our findings show that the factors studied, which are accessibility, enjoyment, technological competence and learning through video games, have an impact on students’ acceptance of video games as an educational tool and how this positive relationship leads to enhanced academic performance in physical education.
Emerging imbalance in the development of TPACK - A challenge for teacher training
Authors: Valtonen Teemu , Eriksson Miikka , Kärkkäinen Sirpa , Tahvanainen Ville , Turunen Anni , Vartiainen Henriikka , Kukkonen Jari , Sointu Erkko 
Published: November 2022

AbstractThis research continues the tradition of Technological Pedagogical Content Knowledge (TPACK) studies aimed at better understanding the development of pre-service teachers’ TPACK. The aim of the study is to show what are the areas of TPACK that pre-service teachers perceived as important and relevant for them, from a teacher training course. This course can be seen as a one of the many teacher training courses within teacher training. The course is not specially designed for mere educational technology, instead the course focuses on biology, using inquiry-based learning activities supported with various technologies. This study was conducted using qualitative methods. The research data consists of pre-service teachers’ (n = 165) answers to two short questions focusing on the elements that pre-service teachers gained from a teacher training science course for building their TPACK. The aim of this method was to highlight only the area that pre-service teachers felt important and relevant without providing any guiding structures. Results show the important role of Pedagogical content Knowledge (PCK) as the core area that respondents gained from the course. The results indicate that the role of Technological Pedagogical Knowledge (TPK) remained modest, the number of responses focusing on TPK was low, and the responses remained at a very general level. The results suggest that in order to provide pre-service teachers with better and more balanced support for the development of their TPACK, we need to highlight technology and make its role more explicit, especially from the perspectives of teachers.
Evaluation of natural products and chemical compounds to improve the control strategy against cucumber powdery mildew
Authors: Elagamey Eman , Abdellatef Magdi A. E. , Haridy Maha S. A. , Abd El-aziz El-Sayed A. E. 
Published: November 2022

AbstractPowdery mildew, caused by Podosphaera xanthii, is a devastating disease that can wipe out a cucumber crop in optimal weather conditions. Disease control management depends mainly on fungicides to inhibit the pathogen. However, they have fatal side effects on most organisms. This study evaluated the use of natural products as safe alternatives to fungicides for controlling cucumber powdery mildew. The effectiveness and phytotoxicity of the studied products, as well as their effects on leaf anatomy and pollen fertility, were evaluated. Although the fungicide tested (Score 25% EC) was the most effective treatment, it caused the highest phytotoxicity, leaf morphological changes, and pollen sterility. All the treatments used significantly reduced disease severity under greenhouse and field conditions, except for Spirulina, which recorded the lowest efficacy rate. Lemon oil, garlic oil, and Blight stop achieved the desired goal of controlling the disease and improving the plant’s physiological state. Therefore, we recommend using any of them to control cucumber powdery mildew, except for Spirulina, which we recommend as a biofertilizer.
Why Limitarianism Fails on its Own Premises – an Egalitarian Critique
Authors: Halldenius Lena 
Published: November 2022

AbstractThis article is a critical analysis of Ingrid Robeyns’ “economic limitarianism” (2017, 2019, 2022), the suggestion that there is a moral case against allowing people to be richer than they need to be in order to achieve full flourishing. Wealth above a certain “riches line” lacks value and should be capped at that level. Robeyns claims that limitarianism is justified as a partial theory of economic justice, since vast wealth is a threat to political equality and the revenue raised from taxing wealth can be used to meet urgent needs. She also claims that limitarianism is problem-driven philosophy and should be judged by its capacity to address problems in the world as it is. The argument in this article is that limitarianism fails on its own premises, both as partial theory of justice and as guide to decision-making. The arguments invoked in its favour as theory does not provide reasons to support it over other redistributive schemes. As guide to practical action, it runs counter to what empirical research reveals about how attitudes to economic inequality works in that people’s acceptance of inequality adjusts: the more unequal a society is, the more inequality is accepted as fair. By disregarding inequalities below the riches-line as well as the economic system that produces them, limitarianism has no tools for countering this adjustment and risks legitimating a politics of inequality. Anyone who shares Robeyns’ concerns about economic inequality have reason to be wary of limitarianism.
Lightening the academic burden on Chinese children: A discourse analysis of recent education policies
Authors: Jiang Weiqi , Saito Eisuke 
Published: November 2022

AbstractThere is increasing acknowledgement of the need to lighten the academic burden on Chinese primary and secondary students because it affects the quality of their education and well-being. This study investigates recent education policies to analyse how the problem of academic burden is framed in these policies and how they attempt to solve it. In this study, Carol Bacchi’s theory of the construction of policy problems and Paul Preuss’ theory of root cause analysis were utilised to provide a productive way to interpret the problem and causes of the academic burden. The data reveal larger social issues related to Chinese educational values perceived by different stakeholders and educational equity necessitating the lightening of academic burden. Therefore, policymakers need to consider the complexity of educational change to effectively reduce the academic burden.
What do monarchs feed on in winter? Nectar sources at hibernation sites
Authors: Sánchez-Tlacuahuac Nancy , Pimentel-Equihua José Luis , Espinosa-Hernández Vicente , Vibrans Heike 
Published: November 2022

Abstract
Introduction
The steep population decline of the emblematic monarch butterfly is caused mainly by the reduction of food sources for caterpillars and adults, as well as disturbance in its overwintering forests. Although feeding at the overwintering sites in Mexico has long been considered unimportant, observations show that butterflies feed inside and outside of their forests on sunny days. Nectaring close to the hibernation colonies may be relevant for their conservation, as their reserves decline faster in disturbed forests. However, there are no systematic studies on nectar sources in the Monarch Butterfly Biosphere Reserve, Michoacán, Mexico.

Aims
In this study, we identified and quantified the main plant species visited by butterflies for nectaring in the Reserve.

Methods
We collected systematic observational data on the flora and butterfly visits in three sanctuaries, around colonies formed between February and March of the 2019–2020 season.

Results
Butterflies fed on 29 plant species from 10 families. Most had white, yellow or blue flowers, were somewhat synanthropic and had their main flowering season in winter. The most visited species were Salvia mexicana and S. plurispicata, which were also the most abundant. By individual plant, Prunus serotina, Crataegus mexicana, Buddleja sessiliflora, Verbesina oncophora and Roldana albonervia were the most visited.

Discussion/Implications for insect conservation
The results point to possible interventions in support of the butterflies. The visited species are generally easy to encourage or cultivate and could be promoted in the surrounding agricultural areas.
Combination of WFDC2, CHI3L1, and KRT19 in Plasma Defines a Clinically Useful Molecular Phenotype Associated with Prognosis in Critically Ill COVID-19 Patients
Authors: Ebihara Takeshi , Matsubara Tsunehiro , Togami Yuki , Matsumoto Hisatake , Tachino Jotaro , Matsuura Hiroshi , Kojima Takashi , Sugihara Fuminori , Seno Shigeto , Okuzaki Daisuke , Hirata Haruhiko , Ogura Hiroshi 
Published: November 2022

Abstract
Background
COVID-19 is now a common disease, but its pathogenesis remains unknown. Blood circulating proteins reflect host defenses against COVID-19. We investigated whether evaluation of longitudinal blood proteomics for COVID-19 and merging with clinical information would allow elucidation of its pathogenesis and develop a useful clinical phenotype.

Methods
To achieve the first goal (determining key proteins), we derived plasma proteins related to disease severity by using a first discovery cohort. We then assessed the association of the derived proteins with clinical outcome in a second discovery cohort. Finally, the candidates were validated by enzyme-linked immunosorbent assay in a validation cohort to determine key proteins. For the second goal (understanding the associations of the clinical phenotypes with 28-day mortality and clinical outcome), we assessed the associations between clinical phenotypes derived by latent cluster analysis with the key proteins and 28-day mortality and clinical outcome.

Results
We identified four key proteins (WFDC2, GDF15, CHI3L1, and KRT19) involved in critical pathogenesis from the three different cohorts. These key proteins were related to the function of cell adhesion and not immune response. Considering the multicollinearity, three clinical phenotypes based on WFDC2, CHI3L1, and KRT19 were identified that were associated with mortality and clinical outcome.

Conclusion
The use of these easily measured key proteins offered new insight into the pathogenesis of COVID-19 and could be useful in a potential clinical application.
Neural networks for estimation of facial palsy after vestibular schwannoma surgery
Authors: Rampp Stefan , Holze Magdalena , Scheller Christian , Strauss Christian , Prell Julian 
Published: November 2022

Abstract
Purpose
Facial nerve damage in vestibular schwannoma surgery is associated with A-train patterns in free-running EMG, correlating with the degree of postoperative facial palsy. However, anatomy, preoperative functional status, tumor size and occurrence of A-trains clusters, i.e., sudden A-trains in most channels may further contribute. In the presented study, we examine neural networks to estimate postoperative facial function based on such features.

Methods
Data from 200 consecutive patients were used to train neural feed-forward networks (NN). Estimated and clinical postoperative House and Brackmann (HB) grades were compared. Different input sets were evaluated.

Results
Networks based on traintime, preoperative HB grade and tumor size achieved good estimation of postoperative HB grades (chi2 = 54.8), compared to using tumor size or mean traintime alone (chi2 = 30.6 and 31.9). Separate intermediate nerve or detection of A-train clusters did not improve performance. Removal of A-train cluster traintime improved results (chi2 = 54.8 vs. 51.3) in patients without separate intermediate nerve.

Conclusion
NN based on preoperative HB, traintime and tumor size provide good estimations of postoperative HB. The method is amenable to real-time implementation and supports integration of information from different sources. NN could enable multimodal facial nerve monitoring and improve postoperative outcomes.
Intimate Partner Violence and Abuse: A Qualitative Exploration of UK Military Personnel and Civilian Partner Experiences
Authors: Lane Rebecca , Gribble Rachael , Alves-Costa Filipa , Taylor Anna , Howard Louise M , Fear Nicola T , MacManus Deirdre 
Published: November 2022

Abstract
Purpose
The prevalence of Intimate Partner Violence and Abuse (IPVA) perpetration and victimisation has been found to be higher in serving and ex-serving military samples compared to civilians. Despite this, there is a lack of qualitative research exploring the IPVA experiences of couples in which one or both partners are serving or have served in the military. This qualitative study aimed to explore IPVA experiences within the UK military community from the perspective of serving and ex-serving military personnel and civilian partners of UK military personnel.

Method
One-to-one telephone interviews were conducted with 40 serving and ex-serving military personnel (29 male, 11 female) and 25 female civilian partners. Data was analysed using thematic analysis.

Results
Four superordinate themes were derived: (1) patterns and directions of IPVA, (2) types of IPVA, (3) perceived drivers of IPVA and (4) perceived impact of IPVA. The findings point to frequent bidirectional abuse in part driven by poor communication and emotion regulation, whilst also highlighting the experiences of severe IPVA victimisation of civilian partners by military personnel motivated by power and control. Perceived drivers of both IPVA perpetration and victimisation include military factors borne of military culture or training, alcohol and mental health difficulties.

Conclusion
These results highlight the role of cultural norms, as well as the role of emotion dysregulation, poor communication skills and mental health difficulties in explaining and perpetuating abuse within ecological theoretical frameworks of violence among couples within which one or both partners are serving or ex-serving military personnel.
Te Pou: An Indigenous Framework to Evaluate the Inclusion of Family Voice in Family Violence Homicide Reviews
Authors: Roguski Michael , Grennell Dennis , Dash Synthia , De’Haan Irene , Cram Fiona , Gulliver Pauline 
Published: November 2022

Abstract

Purpose
Within this paper we evaluate the inclusion of the family voice in the domestic homicide review process. We use an Indigenous rubric (Te Pou) developed to ensure a culturally appropriate framework for conducting fatality reviews. We further draw on the creative potential of Indigenous knowledge systems, applying them alongside Western understandings of the engagement of family in homicide reviews, to seed new knowledge. This review has been undertaken early in the process of implementing family interviews to learn from current practices, seeking to improve them and, thereby, be better hosts for those invited to be part of a review.

Methods
The process of including family input into the in-depth reviews of family violence homicide reviews, conducted between October 2019 and November 2021, was reviewed against the guidelines specified within Te Pou.

Results
While there were strengths within the current process, the review established that further work is required to fully embed cultural understandings and processes within homicide reviews. Indeed, the current process and legislative framework in which it is based, works against relational obligations of reciprocity and a duty to care.

Conclusions
Using indigenous frameworks and research methods, it is possible to determine how homicide review processes have the potential to embed trauma rather than providing an opportunity for critical reflection and healing. Repositioning homicide reviews will require a reconceptualization of the legislative framework and support system requirements for review panels.
Adversarial examples for extreme multilabel text classification
Authors: Qaraei Mohammadreza , Babbar Rohit 
Published: November 2022

AbstractExtreme Multilabel Text Classification (XMTC) is a text classification problem in which, (i) the output space is extremely large, (ii) each data point may have multiple positive labels, and (iii) the data follows a strongly imbalanced distribution. With applications in recommendation systems and automatic tagging of web-scale documents, the research on XMTC has been focused on improving prediction accuracy and dealing with imbalanced data. However, the robustness of deep learning based XMTC models against adversarial examples has been largely underexplored. In this paper, we investigate the behaviour of XMTC models under adversarial attacks. To this end, first, we define adversarial attacks in multilabel text classification problems. We categorize attacking multilabel text classifiers as (a) positive-to-negative, where the target positive label should fall out of top-k predicted labels, and (b) negative-to-positive, where the target negative label should be among the top-k predicted labels. Then, by experiments on APLC-XLNet and AttentionXML, we show that XMTC models are highly vulnerable to positive-to-negative attacks but more robust to negative-to-positive ones. Furthermore, our experiments show that the success rate of positive-to-negative adversarial attacks has an imbalanced distribution. More precisely, tail classes are highly vulnerable to adversarial attacks for which an attacker can generate adversarial samples with high similarity to the actual data-points. To overcome this problem, we explore the effect of rebalanced loss functions in XMTC where not only do they increase accuracy on tail classes, but they also improve the robustness of these classes against adversarial attacks. The code for our experiments is available at https://github.com/xmc-aalto/adv-xmtc.
Robust Optimal Well Control using an Adaptive Multigrid Reinforcement Learning Framework
Authors: Dixit Atish , Elsheikh Ahmed H. 
Published: November 2022

AbstractReinforcement learning (RL) is a promising tool for solving robust optimal well control problems where the model parameters are highly uncertain and the system is partially observable in practice. However, the RL of robust control policies often relies on performing a large number of simulations. This could easily become computationally intractable for cases with computationally intensive simulations. To address this bottleneck, an adaptive multigrid RL framework is introduced which is inspired by principles of geometric multigrid methods used in iterative numerical algorithms. RL control policies are initially learned using computationally efficient low-fidelity simulations with coarse grid discretization of the underlying partial differential equations (PDEs). Subsequently, the simulation fidelity is increased in an adaptive manner towards the highest fidelity simulation that corresponds to the finest discretization of the model domain. The proposed framework is demonstrated using a state-of-the-art, model-free policy-based RL algorithm, namely the proximal policy optimization algorithm. Results are shown for two case studies of robust optimal well control problems, which are inspired from SPE-10 model 2 benchmark case studies. Prominent gains in computational efficiency are observed using the proposed framework, saving around 60-70% of the computational cost of its single fine-grid counterpart.