bluuebunny/crossref_metadata_2025_split huggingface dataset:
Breaking Taboos: Arab Breast Cancer Activism in Art and Popular Culture
Authors: Hamdar Abir 
Published: September 2024

AbstractThis essay examines the breast cancer accounts of four Arab female celebrities who have spoken out in public about their illness experience: the Egyptian TV presenter Basma Wahba and the actress Yasmine Ghaith, the Iraqi actress Namaa al-Ward, and the Lebanese pop singer Elissa. By reading their testimonies against the backdrop of critical literature on illness narratives and memoirs, as well as on cancer narratives and activism, the essay asks: how are the accounts of these women’s cancer diagnosis and treatment disclosed and described? In what medium do they communicate and circulate their breast cancer experiences? What significance do these public disclosures have on challenging and breaking the Arab taboo of cancer? In conclusion, the essay argues that these women’s willingness to share their stories in public constitutes an important form of multimedia activist intervention—visual, sonic, and performative—that is playing a key role in the development of a breast cancer movement.
The impact of demographic change on value set validity and obsolescence
Authors: Jonker Marcel F. 
Published: September 2024

Abstract
Purpose
To investigate the contribution of demographic trends in countries’ age and gender composition to value set validity and obsolescence.

Methods
Time-trade off (TTO) valuation data from 3 EQ-5D-3L value sets of 20 years or older from the United Kingdom, Japan, and the United States were re-analyzed using Bayesian heteroskedastic Tobit models with sex and age group-specific scale parameters. Original value sets were obtained by weighting the original preference structures with the countries’ original demographic composition at the time of the data collection. Updated value sets were created using the original preference structure weighted using the countries’ most recent demographic composition. The differences between the original and updated value sets were monitored and compared based on 95% credible intervals.

Results
The gender and age composition of the investigated countries changed in all 3 countries over time. The modelled health state preferences also depended on the respondents’ gender and age. However, the overall impact of this demographic change on the investigated value sets was negligeable in all 3 countries and this finding was robust to accounting for the impact of ethnicity trends in the United States.

Conclusion
Value sets may become redundant and obsolete for various reasons, but demographic change was not identified as a contributing factor.
Digital Transformations Through the Lens of the Collaborative, Co-Generative and Domesticative
Authors: Landmark Andreas D. , Ravn Johan E. , Torvatn Hans Y. , Øyum Lisbeth 
Published: September 2024

AbstractWe explore digital transformation and socio-technical systems through the perspectives of collaboration, co-generation, and domestication. Building on Morten Levin’s influential work, we discuss how digital technologies are integrated into work environments, emphasising the necessity for democratic, participatory approaches. We discuss how his ideas of collaborative, democratic practice originating in the 1990s hold up to today’s organisational challenges like digital and sustainability transitions. Are the ideas and practices still valid, or do we need to update them? We identify the grand challenges that must be met: Ensuring sustainable digital transitions and developing educated, skilled workers in digitally transformed organisations.
Venous thrombosis and obesity: from clinical needs to therapeutic challenges
Authors: La Rosa Federica , Montecucco Fabrizio , Liberale Luca , Sessarego Marta , Carbone Federico 
Published: September 2024

Abstract
Weight bias and stigma have limited the awareness of the systemic consequences related to obesity. As the narrative evolves, obesity is emerging as a driver and enhancer of many pathological conditions. Among these, the risk of venous thromboembolism (VTE) is a critical concern linked to obesity, ranking as the third most common cardiovascular condition. Obesity is recognized as a multifactorial risk factor for VTE, influenced by genetic, demographic, behavioral, and socio-economic conditions. Despite established links, the exact incidence of obesity related VTE in the general population remains largely unknown. The complexity of distinguishing between provoked and unprovoked VTE, coupled with gaps in obesity definition and assessment still complicates a tailored risk assessment of VTE risk. Obesity reactivity, hypercoagulability, and endothelial dysfunction are driven by the so-called ‘adiposopathy’. This state of chronic inflammation and metabolic disturbance amplifies thrombin generation and alters endothelial function, promoting a pro-thrombotic environment. Additionally, the inflammation-induced clot formation—also referred to as ‘immunothrombosis’ further exacerbates VTE risk in people living with obesity. Furthermore, current evidence highlights significant gaps in the management of obesity related VTE, particularly concerning prophylaxis and treatment efficacy of anticoagulants in people living with obesity. This review underscores the need for tailored therapeutic approaches and well-designed clinical trials to address the unique challenges posed by obesity in VTE prevention and management. Advanced research and innovative strategies are imperative to improve outcomes and reduce the burden of VTE in people living with obesity.
Automated Bayesian variable selection methods for binary regression models with missing covariate data
Authors: Bergrab Michael , Aßmann Christian 
Published: September 2024

AbstractData collection and the availability of large data sets has increased over the last decades. In both statistical and machine learning frameworks, two methodological issues typically arise when performing regression analysis on large data sets. First, variable selection is crucial in regression modeling, as it helps to identify an appropriate model with respect to the considered set of conditioning variables. Second, especially in the context of survey data, handling of missing values is important for estimation, which occur even with state-of-the-art data collection and processing methods. Within this paper, we provide an Bayesian approach based on a spike-and-slab prior for the regression coefficients, which allows for simultaneous handling of variable selection and estimation in combination with handling of missing values in covariate data. The paper also discusses the implementation of the approach using Markov chain Monte Carlo techniques and provides results for simulated data sets and an empirical illustration based on data from the German National Educational Panel Study. The suggested Bayesian approach is compared to other statistical and machine learning frameworks such as Lasso, ridge regression, and Elastic net, and is shown to perform well in terms of estimation performance and variable selection accuracy. The simulation results demonstrate that ignoring the handling of missing values in data sets can lead to the generation of biased selection results. Overall, the proposed Bayesian method offers a holistic, flexible, and powerful framework for variable selection in the presence of missing covariate data.
Safer or Endangered at Home?: An Examination of Neighborhood Effects on Family Violence Before, During, and After the COVID-19 Safer-at-Home Order
Authors: Harris Michelle N. , Konkel Rebecca H. 
Published: September 2024

AbstractResearch has begun to document the impact of COVID-19, and corresponding restrictions associated with the pandemic, to several unintended consequences including increased rates of family violence. Given these findings, there have been calls for research to understand predictors of family violence, and how such predictors may shift during differing periods of the pandemic. Rooted in the family stress model and social disorganization theory, this study used call for service data from two police departments located in the Midwest of the United States, to examine whether the relationship between sociostructural indices on the number of family violence incidents varied in the times before, during, and after COVID-19 Safer-at-Home orders. Based on a series of negative binomial regression models, results show that the association between neighborhood (i.e., Census block group) concentrated poverty, the percentage of the population between 15 and 24 years of age, and the percentage of the population that identify as male on family violence was amplified during and/or after the adoption of the Safer-at-Home order. Results also indicated that the Safer-at-Home order eradicated the once significant effect of residential mobility and family violence incidents. Lastly, although neighborhoods located within the more populous and metropolitan city experienced fewer family violence incidents, compared to the mixed rural-suburban city, the gap in the number of incidents per neighborhood decreased in the times following the enactment of the Safer-at-Home order. Based on these findings, proactive and reactive policies regarding resource dissemination and increasing neighboring activities are discussed.
Quantifying the morphology of rock joints and updating the JRC–JCS criterion considering the asperity distribution
Authors: Wang Feili , Meng Fanzhen , Wang Shuhong , Xiu Zhanguo 
Published: September 2024

AbstractRoughness ubiquitously prevails in rock joints and controls the shear behaviours, permeability and damage characteristics of rock joints. A plethora of investigations have focused on the description of joint roughness; however, a detailed method for quantifying joint roughness and evaluating the shear strength has not yet been established. In this study, within the framework of fractal theory, an optical measurement scale was defined to depict the fractal characteristics of joint roughness, and a boundary measurement scale was used to identify first- and second-order asperities. A composite indicator $$\eta$$
η
, including the fractal roughness coefficient ($$R_{D}$$

R
D

), the coefficient describing the roughness order ($$\mu$$
μ
) and the anisotropy parameter ($$\lambda$$
λ
), was proposed to quantify the surface morphology, which takes the asperity distribution and roughness anisotropy into account. The relationship between $$\eta$$
η
and JRC was established, and the JRC–JCS criterion was further updated. Moreover, representative examples were given to show the steps required to quantify the morphology of rough joint surfaces with the new quantitative parameter. Direct shear tests were conducted to validate the effectiveness of the proposed method in describing joint roughness and estimating joint shear strength; the results indicate that it is appropriate to use $$\eta$$
η
to estimate the joint roughness and that the proposed shear strength criterion can appropriately predict the shear strength within an acceptable error.
Can a Good Theory Be Built Using Bad Ingredients?
Authors: Field Sarahanne M. , Volz Leonhard , Kaznatcheev Artem , van Dongen Noah 
Published: September 2024

AbstractThe replication crisis threatens to seriously impact theory development in the cognitive, behavioral, and social sciences. We canvas three desiderata of scientific theories (explanation, prediction, and unification) and argue that the extent to which failures of replication prove problematic depends on the primary purpose of a theory. If the aim is to explain how nature works, then accuracy—and thus replicability—of the findings on which the theory is built is essential. If the aim is to predict outcomes, then replicability of findings from which the predictive model or theory is built is only important as far as it affects the reliability and accuracy of the predictions. If the aim is to unify and organize disparate findings, then the replicability of findings plays a non-essential role. The result is that a multifaceted and nuanced perspective is required to assess the value of replicability and the need for replication studies. Specifying a theory’s purpose and background commitments should clarify the debate on replication and contribute to better theory development in the cognitive, behavioral, and social sciences.
Improvement of seismic performance of concrete structures using magnetized concrete: an empirical study
Authors: Hashemi Shahroodi Seyed Yousef 
Published: September 2024

AbstractHigh-Performance Concrete (HPC) offers better workability, compressive strength, and durability compared to ordinary concrete. One type of high-strength concrete is magnetic concrete. The primary objective of this empirical research is to investigate the potential of magnetized concrete in enhancing the seismic properties of concrete structures, in this technology, the physical structure of water is altered by inducing a magnetic field. The research employs empirical methods, including experimental testing and analysis, to assess the seismic performance of magnetized concrete compared to traditional concrete. As a result, the number of molecules in a molecular cluster decrease from 13 to 5 or 6, and the surface tension of the water is reduced. Using this water in concrete preparation increases the workability of the concrete mix and reduces the required water. Additionally, by facilitating cement hydration, it increases the compressive strength and durability of the concrete. This increased compressive strength enhances the stability and resistance of the structure against earthquake forces. Moreover, maintaining the compressive strength of the concrete while using magnetic water can reduce cement consumption. This reduction in cement consumption can lower the weight of the structure, which is very effective in reducing earthquake forces in tall buildings.
A comprehensive survey of fast graph clustering
Authors: Xue Jingjing , Xing Liyin , Wang Yuting , Fan Xinyi , Kong Lingyi , Zhang Qi , Nie Feiping , Li Xuelong 
Published: September 2024

AbstractGraph clustering methods are popular due to their ability to discover clusters with arbitrary shapes. However, with the emergence of large-scale datasets, the efficiency of graph clustering algorithms has become a significant concern. As a result, many researchers have been drawn to the field of fast graph clustering algorithms, leading to rapid and intricate advancements in related research. Nevertheless, there is currently no comprehensive survey available for fast graph clustering algorithms. To address this gap, we review these fast graph clustering models in both single and multi-view fields, categorizing them based on different properties and analyzing their advantages and disadvantages. In the single-view field, the main categories we explore include large graph methods and bipartite graph methods. The former includes graph cut and graph density methods, while the latter includes graph cut, co-clustering, and label transmission methods. For the multi-view field, the main categories also include large graph methods and bipartite graph methods. The former is specifically designed to avoid the eigenvalue decomposition of graph cut models, and the latter focuses on accelerating algorithms by integrating anchor points. Towards the conclusion of this paper, we discuss the challenges and provide several further research directions for fast graph clustering.
Open Exceptions: Why Does the Brazilian Health Regulatory Agency (ANVISA) Exempts RIA and Ex Post Reviews?
Authors: Salinas Natasha Schmitt Caccia , Gomes Lucas Thevenard 
Published: September 2024

AbstractIn this article, we examine the evolving landscape of Regulatory Impact Assessment (RIA) decisions made by the National Health Regulatory Agency (Agência Nacional de Vigilância Sanitária [Anvisa]), a prominent federal agency leading RIA implementation in Brazil. We quantify Anvisa’s RIA usage rates, exploring the influence of emergency and other justifications for RIA exemptions both pre and post the enactment of detailed procedural requirements in a recent Presidential Order No. 10,411/2020. Our quantitative analysis shows a sharp decline in RIA usage after Presidential Order No. 10,411/2020 came into force in April 2021, as well as a diversification of the justifications given in decisions not to use RIA (exception decisions). This effect is present even if we take into consideration a large proportion of exception decisions tied to Anvisa’s regulatory stock review and to urgent measures prompted by the COVID-19 pandemic. Additionally, we conduct a qualitative analysis of exception decisions due to emergency, post-Presidential Order. We find that Anvisa failed to provide compelling justifications for exempting RIA in emergency regulations in several cases and avoids ex post reviews when RIAs are waived due to emergencies. We conclude the article with recommendations to enhance the scrutiny and transparency of Anvisa’s exception decisions to conduct RIA.
Constructing ensembles for hate speech detection
Authors: Kucukkaya Izzet Emre , Toraman Cagri 
Published: September 2024

Abstract
Hate speech against individuals and groups with certain demographics is a major issue in social media. Supervised models for hate speech detection mostly utilize labeled data collections to understand textual semantics. However, hate speech detection is a complex task that involves several aspects, including topic and writing style. The complexity of hate speech can be represented by an ensemble of models learned from different aspects of data. Moreover, ensemble members or base models can be modified to give attention to particular aspects of hate speech. In this study, we extract different aspects of hate speech to construct ensembles, thereby improving the performance of hate speech detection by ensemble learning. We conduct detailed experiments on five datasets in multiple languages to generalize our observations. The experimental results, supported by statistical significance tests, show that the performance of hate speech detection can be improved by capturing multiple aspects of hate speech. Our ensemble construction approach outperforms the baselines in terms of the F1 score of the Hate class in 80% of the cases, and the Offensive class in 75% of the cases. We also compare our approach with state-of-the-art ensemble methods from shared tasks and find that our highest-performing method can improve the performance of the Hate class in two out of three datasets. We further discuss our approach and experimental results in terms of ensemble parameters and writing style among ensemble members.
Can social investment empower social innovation? A comparative discourse analysis of UK and Scottish policy
Authors: Purna Nadeen , Tuckerman Lauren , Abdul Rahman Syahirah 
Published: September 2024

Abstract
Social investment can act as an empowering funding mechanism that could activate the economic agency of marginalised people while addressing their social needs. Nonetheless, political agendas might cause divergence in the achievement of social investment’s potential benefits. To develop our understanding in this area, this paper aims to extract discursive policy framings of social investment by comparing the UK and Scottish Government policies to identify the use of social investment and its implications on social innovation. Using corpus linguistic methods that allows for a framing analysis, the paper’s findings are twofold. Firstly, both the UK and Scottish Governments share similarities in the framing of social investment policy, especially in the proclivity towards the privatisation of social welfare delivery using market mechanisms. Secondly, the governments differ in their intensity of conviction for social investment which creates divergent implications for social innovation practice in the countries.
Thinking freedom relationally: Life projects and care as an ethical orientation for International Studies
Authors: FitzGerald Maggie 
Published: September 2024

Abstract
The field of International Studies has often been concerned with either negative conceptualisations of freedom and liberty (i.e. freedom from obstacles and interference) or positive notions of freedom (i.e. the possibility to act and develop). Further, these two notions of freedom have been conceived of as rival and incompatible. Drawing upon Simone de Beauvoir’s ethics of ambiguity (1947), this article rejects such a binary conceptualisation of freedom and instead puts forward a relational understanding of freedom. This article also begins to sketch the possibilities offered by such an understanding of freedom via a nascent dialogue between this relational freedom and the ethics of care. Specifically, it is posited that care and freedom weave together to form the very ethical space and conditions in and through which in becomes possible to pursue various life projects in the first place. Care and freedom, it is suggested, may thus provide one orientation for studying and practising international relations in a manner that moves towards building, amending, and maintaining relations that better support everyone (where this, crucially, also entails the ending of relations which oppress, harm, and cause suffering).
An online payload identification method based on parameter difference for industrial robots
Authors: Xu Tian , Tuo Hua , Fang Qianqian , Chen Jie , Fan Jizhuang , Shan Debin , Zhao Jie 
Published: September 2024

AbstractAccurate online estimation of the payload parameters benefits robot control. In the existing approaches, however, on the one hand, only the linear friction model was used for online payload identification, which reduced the online estimation accuracy. On the other hand, the estimation models contain much noise because of using actual joint trajectory signals. In this article, a new estimation algorithm based on parameter difference for the payload dynamics is proposed. This method uses a nonlinear friction model for the online payload estimation instead of the traditionally linear one. In addition, it considers the commanded joint trajectory signals as the computation input to reduce the model noise. The main contribution of this article is to derive a symbolic relationship between the parameter difference and the payload parameters and then apply it to the online payload estimation. The robot base parameters without payload were identified offline and regarded as the prior information. The one with payload can be solved online by the recursive least squares method. The dynamics of the payload can be then solved online based on the numerical difference of the two parameter sets. Finally, experimental comparisons and a manual guidance application experiment are shown. The results confirm that our algorithm can improve the online payload estimation accuracy (especially the payload mass) and the manual guidance comfort.