bluuebunny/crossref_metadata_2025_split huggingface dataset:
Media Exposure to Highly Skilled Immigrants and Attitudes Toward Immigration
Authors: Unan Asli 
Published: November 2024

AbstractCan exposure to successful immigrants in the mass media affect perceptions of immigrants and alter attitudes toward immigration? To address this question, I study the case of Ozlem Tureci and Ugur Sahin, the co-developers of the Pfizer-BioNTech COVID-19 vaccine, and children of Turkish immigrants in Germany. I first demonstrate that German media favorably highlighted the Turkish roots and migration history of vaccine developers. Leveraging the quasi-experimental setting with the announcement of the success of the vaccine, I then posit that the wide broadcasting of the vaccine’s success and its developers’ identity should have positive spillover effects on public attitudes towards immigration. Amongst those who were exposed to this announcement, compared to those who were not, there is a 4 percentage-point increase in support of easing immigration opportunities. I suggest that this effect is driven by a change in perceptions and knowledge on issues relating to immigration and integration. These findings imply that promoting successful immigrants in the media has the potential to diminish prejudice; however, it is crucial to address varying biases toward different immigrant groups.
Deep reinforcement learning-based spatio-temporal graph neural network for solving job shop scheduling problem
Authors: Gebreyesus Goytom , Fellek Getu , Farid Ahmed , Hou Sicheng , Fujimura Shigeru , Yoshie Osamu 
Published: November 2024

Abstract
The job shop scheduling problem (JSSP) is a well-known NP-hard combinatorial optimization problem that focuses on assigning tasks to limited resources while adhering to certain constraints. Currently, deep reinforcement learning (DRL)-based solutions are being widely used to solve the JSSP by defining the problem structure on disjunctive graphs. Some of the proposed approaches attempt to leverage the structural information of the JSSP to capture the dynamics of the environment without considering the time dependency within the JSSP. However, learning graph representations only from the structural relationship of nodes results in a weak and incomplete representation of these graphs which does not provide an expressive representation of the dynamics in the environment. In this study, unlike existing frameworks, we defined the JSSP as a dynamic graph to explicitly consider the time-varying aspect of the JSSP environment. To this end, we propose a novel DRL framework that captures both the spatial and temporal attributes of the JSSP to construct rich and complete graph representations. Our DRL framework introduces a novel attentive graph isomorphism network (Attentive-GIN)-based spatial block to learn the structural relationship and a temporal block to capture the time dependency. Additionally, we designed a gated fusion block that selectively combines the learned representations from the two blocks. We trained the model using the proximal policy optimization algorithm of reinforcement learning. Experimental results show that our trained model exhibits significant performance enhancement compared to heuristic dispatching rules and learning-based solutions for both randomly generated datasets and public benchmarks.
Euler–Lagrange equation for gradient-type Lagrangian and related conservation laws
Authors: Treanţă Savin , Dragu Marius-Alin 
Published: November 2024

AbstractVariational calculus with gradient-type variations has often been neglected, although it proves to be suitable for certain concrete problems governed by several evolution variables. These kinds of variations lead to Euler–Lagrange partial differential equations controlled by the right-hand member. In this context, we also introduce anti-trace Euler–Lagrange partial differential equations that are suitable for some innovative ideas. Also, some applications are provided for the theoretical results derived in the paper.
Response of drought to climate extremes in a semi-arid inland river basin in China
Authors: Qu Zhicheng , Yao Shunyu , Liu Dongwei 
Published: November 2024

AbstractAgainst the backdrop of global warming, climate extremes and drought events have become more severe, especially in arid and semi-arid areas. This study forecasted the characteristics of climate extremes in the Xilin River Basin (a semi-arid inland river basin) of China for the period of 2021–2100 by employing a multi-model ensemble approach based on three climate Shared Socioeconomic Pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) from the latest Coupled Model Intercomparison Project Phase 6 (CMIP6). Furthermore, a linear regression, a wavelet analysis, and the correlation analysis were conducted to explore the response of climate extremes to the Standardized Precipitation Evapotranspiration Index (SPEI) and Streamflow Drought Index (SDI), as well as their respective trends during the historical period from 1970 to 2020 and during the future period from 2021 to 2070. The results indicated that extreme high temperatures and extreme precipitation will further intensify under the higher forcing scenarios (SSP5-8.5>SSP2-4.5>SSP1-2.6) in the future. The SPEI trends under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios were estimated as −0.003/a, −0.004/a, and −0.008/a, respectively, indicating a drier future climate. During the historical period (1970–2020), the SPEI and SDI trends were −0.003/a and −0.016/a, respectively, with significant cycles of 15 and 22 a, and abrupt changes occurring in 1995 and 1996, respectively. The next abrupt change in the SPEI was projected to occur in the 2040s. The SPEI had a significant positive correlation with both summer days (SU) and heavy precipitation days (R10mm), while the SDI was only significantly positively correlated with R10mm. Additionally, the SPEI and SDI exhibited a strong and consistent positive correlation at a cycle of 4–6 a, indicating a robust interdependence between the two indices. These findings have important implications for policy makers, enabling them to improve water resource management of inland river basins in arid and semi-arid areas under future climate uncertainty.
Neuropsychology of sexuality in older adults: bridging gaps in literature and future directions in research
Authors: Taccini Federica , Rossi Margherita , Mannarini Stefania , De Rui Marina , Ceolin Chiara , Volpe Biancarosa , Sarlo Michela , Mapelli Daniela , Sergi Giuseppe , Devita Maria 
Published: November 2024

AbstractSexuality is a fundamental part of human existence and it encompasses thoughts, desires, behaviors, relationships, as well as neuropsychological and physiological components. However, sexuality in older adults is under-researched from the neuropsychological and psychophysiological perspectives and is often neglected by healthcare providers in the clinical practice. This article aims to explore the state of the art on the neuropsychology and psychophysiology of older adults’ sexuality, proposing future research directions and emphasizing its significance. By summarizing current knowledge on the sexuality of younger individuals, it was possible to lay the groundwork for formulating research questions about older adults’ sexuality. The implications proposed in this article will potentially impact both the scientific and also the clinical field. In fact, gaining insights on the neuropsychological and psychophysiological aspects of sexuality in healthy older adults can also shed light into those with neurocognitive disorders.
On the distribution of ocean wave crest heights in varying wave conditions
Authors: Vanem Erik , Gramstad Odin , Babanin Alexander , De Bin Riccardo , Trulsen Karsten 
Published: November 2024

AbstractThis paper addresses the statistical distribution of wave crest heights in ocean environments. This is much needed in several engineering applications including risk and reliability assessment of marine and coastal structures and is an important input for design of ocean structures. However, even though crest height distributions have received a lot of attention within both academia and the industry, current state-of-the-art models have notable shortcomings and do not describe the distribution of crest heights well in all sea states. In particular, commonly used models do not accurately describe the upper tail in certain unstable wave conditions. This will be addressed in this paper, which investigates in what sea-state conditions, established crest height distributions describe the data well and under what conditions, these models fail to fit well to the data. A large dataset of crest heights observed in different sea states is analyzed, and various goodness-of-fit tests are applied to determine when the different statistical models are appropriate. Then rejection rates of the various statistical models will be linked to selected sea-state parameters to identify wave conditions where alternative statistical models are needed. Results from this study indicate that the well-known Forristall distribution, which is known to capture second-order effects well, fails to perform well in some sea states. Different variants of the Tayfun–Fedele distribution are also analyzed, and results suggest that this represent a slight improvement. Somewhat surprisingly, sea-state parameters such as significant wave height ($$H_S$$

H
S

), significant wave steepness ($$S_1$$

S
1

), and the Benjamin–Feir index (BFI) do not seem to be very influential on the probability of rejecting these distributions, even though empirical skewness and kurtosis play a significant role. The implications of this for describing the distribution of crest heights well in certain sea-state conditions are discussed.
Classical ensembles of single-qubit quantum variational circuits for classification
Authors: McFarthing Shane , Pillay Anban , Sinayskiy Ilya , Petruccione Francesco 
Published: November 2024

AbstractThe quantum asymptotically universal multi-feature (QAUM) encoding architecture was recently introduced and showed improved expressivity and performance in classifying pulsar stars. The circuit uses generalized trainable layers of parameterized single-qubit rotation gates and single-qubit feature encoding gates. Although the improvement in classification accuracy is promising, the single-qubit nature of this architecture, combined with the circuit depth required for accuracy, limits its applications on NISQ devices due to their low coherence times. This work reports on the design, implementation, and evaluation of ensembles of single-qubit QAUM classifiers using classical bagging and boosting techniques. We demonstrate an improvement in validation accuracy for pulsar star classification. We find that this improvement is not dataset specific as we observe consistent improvements for the MNIST Digits and Wisconsin Cancer datasets. We also observe that the boosting ensemble achieves an acceptable level of accuracy with only a small amount of training, while the bagging ensemble achieves higher overall accuracy with ample training time. This shows that classical ensembles of single-qubit circuits present a new approach for certain classification problems.
Food, energy, and water security sustainability; an asymmetric approach for achieving sustainable development goals
Authors: Aliyu Umar Shuaibu , Cavusoglu Behiye , Hassanie Souad , Abba Sani I. , Sammen Saad Sh. , Kisi Ozgur 
Published: November 2024

AbstractThe world has arguably identified both output and population growth over time. Thus, the demand for agricultural commodities such as biofuels and the prominent challenges associated with sustainable land use, water, and food development all play a significant role in the development process, as all countries strive to achieve the use of different approaches, such as convergence in output per capita, energy use, water availability, and food production, more safely and ecologically. Therefore, this study is led by Access and Neo-Malthusian theories to investigate the effects of food, energy, and water security sustainability for achieving sustainable development goals using annual data covering 1980 to 2020 to estimate the asymmetric autoregressive distributive lag approach. Accordingly, the results revealed that the effects of positive changes in food, energy, and water security on sustainable development differ significantly from those of negative changes. A 1% change in food production, water supply development, or industrial value addition has a significant positive impact on sustainable development; however, a 1% change in energy use and carbon emission, population growth, or depletion of natural resources has substantial adverse effects on sustainable economic development in both the short run and the long run in Nigeria. The findings also revealed that restoring equilibrium convergence to achieve 2030 SDGs requires 16.2% each year. The study recommends adopting innovative systems that control and help manage available human and natural resources in productive sectors and increase value-added and renewable energy production in Nigeria.
Rumor detection using dual embeddings and text-based graph convolutional network
Authors: Pattanaik Barsha , Mandal Sourav , Tripathy Rudra M. , Sekh Arif Ahmed 
Published: November 2024

AbstractSocial media platforms like Twitter and Facebook have gradually become vital for communication and information exchange. However, this often leads to the spread of unreliable or false information, such as harmful rumors. Currently, graph convolutional networks (GCNs), particularly TextGCN, have shown promise in text classification tasks, including rumor detection. Their success is due to their ability to identify structural patterns in rumors and effectively use neighborhood information. We present a novel rumor detection model using TextGCN, which utilizes a word-document graph to represent rumor texts. This model uses dual embedding from two pre-trained transformer models: generative pre-trained transformers (GPT) and bidirectional encoder representations from transformers (BERT). These embeddings serve as node representations within the graph, enhancing rumor detection. Combining these deep neural networks effectively extracts significant contextual features from rumors. This graph undergoes convolution, and through graph-based learning, the model detects a rumor. We evaluated our model using publicly available rumor datasets, such as PHEME, Twitter15, and Twitter16. It achieved 88.64% accuracy on the PHEME dataset, surpassing similar models, and performed well on Twitter15 and Twitter16 with accuracies of 81.98% and 83.41%, respectively.
Perinatal stress and anxiety in Ireland: experiences and support needs
Authors: Looney Eibhlin , Houghton Catherine , Redsell Sarah , Matvienko-Sikar Karen 
Published: November 2024

Abstract

Objectives:
Perinatal stress and anxiety from conception to two years postpartum have important adverse outcomes for women and infants. This study examined (i) women’s perception of sources and experiences of perinatal stress and anxiety, (ii) women’s attitudes to and experiences of available supports, and (iii) women’s preferences for perinatal stress and anxiety supports in Ireland.

Methods:
An online mixed-methods cross-sectional survey was conducted with 700 women in Ireland. Participants were pregnant women (n = 214) or mothers of children ≤ 2 years old (n = 486). Participants completed closed-ended questionnaires on sociodemographic, birth and child factors, and on stress, anxiety, perceived social support, and resilience. Participants completed open-ended questions about experiences of stress and anxiety and the supports available for stress and anxiety during pregnancy and/or postpartum. Quantitative data were analysed descriptively and using correlations; qualitative data were analysed using thematic analysis.

Results:
Quantitative data indicated significant relationships between perinatal stress and/or anxiety and women’s perceived social support, resilience, having a previous mental health disorder diagnosis (both p < 0.001), and experiencing a high-risk pregnancy or pregnancy complications (p < 0.01). Themes developed in qualitative analyses included: ‘perceived responsibilities’; ‘self-care’; ‘care for maternal health and well-being’; ‘social support’; and ‘access to support and information’.

Conclusions:
Women’s stress and anxiety are impacted by multiple diverse factors related to the individual, to interpersonal relationships, to perinatal health and mental health outcomes, and to available services and supports. Development of support-based individual-level interventions and increased peer support, coupled with improvements to service provision is needed to provide better perinatal care for women in Ireland.
Neurodiversity and inclusive social value management practices
Authors: Ratten Vanessa 
Published: November 2024

Abstract
The neurodiversity movement takes a societal view of individual differences by suggesting that people should be respected and not necessarily medically treated based on personal attributes. This commentary article discusses how human differences in intellectual capacities should be considered as another form of diversity with the requirement for medical intervention needing to be considered in terms of overall social change. As a significant portion of the overall workforce could be considered as people with some form of neurological disability this article analyses how co-creation processes occur meaning neurodiverse individuals should be accepted in society regardless of their differences. This article contributes to societal discussions around managing diversity as in society the socio-demographic categories such as age and gender are well established, but newer categories such as neurodiversity are required.
Space Environmental Effects on Multifunctional Radiation Shielding Materials
Authors: Sen Subhayu , O’Dell John Scott , Yan Yongzhe , Heilbronn Lawrence , Ning Haibin , Finckenor Miria , Carrico Meghan , Pillay Selvum 
Published: November 2024

AbstractThe two primary material requirements for a crewed habitat or spacecraft to operate beyond low earth orbit (LEO) include effective radiation shielding against the space radiation and secondary neutron environment and sufficient structural and thermal integrity. In this context it is mandatory to study the effect of long duration space environment on any proposed multifunctional radiation shielding material. In this paper we discuss two radiation shielding composite architectures and their long duration performance in LEO. Samples were flown on NASA's The Materials International Space Station Experiment (MISSE) platform and their structural, optical, and radiation shielding capabilities were characterized pre and post flight. Results showed composite architecture can be key in determining expected damage irrespective of sample placement orientation on the space station. A surface layer with a protective or sacrificial coating can be instrumental in minimizing property degradation even when exposed to orientations with high estimated sun hours and high fluence of atomic oxygen.
The West Pacific Teleconnection Drives the Interannual Variability of Autumn Wildfire Weather in the Western United States After 2000
Authors: Liu Shizuo , Hu Shineng , Seager Richard 
Published: November 2024

AbstractWildfires pose a significant threat to human society as severe natural disasters. The western United States (US) is one hotspot that has experienced dramatic influences from autumn wildfires especially after 2000, but what has caused its year‐to‐year variations remains poorly understood. By analyzing observational and atmospheric reanalysis datasets, we found that the West Pacific (WP) pattern centered in the western North Pacific acted as a major climatic factor to the post‐2000 autumn wildfire activity by inducing anomalous high pressure over the western US via teleconnections with increased surface temperature, decreased precipitation, and reduced relative humidity. The WP pattern explains about one‐third of the post‐2000 years‐to‐year variance of the western US autumn wildfires. These effects were found to be much weaker in the 1980–1990s, as the active region of WP‐associated high pressure was confined to the eastern North Pacific. Such eastward shift of the WP teleconnection pattern and its resultant, enhanced influence on the weather conditions of western US autumn wildfire after 2000 are also captured by the sea surface temperature (SST)‐forced atmospheric model simulations with the Community Atmosphere Model version 6 (CAM6). The CAM6 ensemble‐mean changes in the WP teleconnection pattern at 2000 is about half of the observed changes, which implies that external radiative forcing and/or SST changes have played an important role in the WP pattern shift. Our results highlight a pressing need to consider the joint impacts of atmospheric internal variability and externally forced climate changes when studying the interannual variations of wildfire activity.
Hydroacoustic Observations Reveal Drivers of Mixing and Salinization of a Karst Subterranean Estuary During Intense Precipitation
Authors: Ganju Neil K. , Pohlman John W. , Suttles Steven E. , Brankovits David 
Published: November 2024

AbstractKarst subterranean estuaries within globally ubiquitous carbonate aquifers are coastal groundwater ecosystems that provide an essential water resource for human populations. To understand the drivers of salinization within a coastal aquifer in the Yucatan Peninsula (Mexico), we employed hydroacoustics in flooded caves to observe how oceanic and atmospheric events facilitate mixing between the meteoric lens (fresh‐brackish groundwater) and the saline groundwater on tidal and episodic timescales. Precipitation during Tropical Storm Carlotta increased the flow and salinity of the meteoric lens without evidence for vertical mixing across the halocline. We postulate that vertical migration of haloclines in the conduit relative to those within the rock matrix during precipitation creates lateral density gradients that drive mixing, and ultimately creates a brackish layer within the meteoric lens. These results provide a mechanistic explanation for vertical and lateral exchange in a coastal carbonate aquifer, which has implications for groundwater response to future climatic change.
Satellite Geodesy Uncovers 15 m of Slip on a Detachment Fault Prior to the 2018 Collapse at Anak Krakatau, Indonesia
Authors: Kim Young Cheol , Wauthier Christelle , Walter Thomas R. 
Published: November 2024

AbstractOn 22 December 2018, parts of the Anak Krakatau edifice collapsed, triggering a deadly tsunami. To investigate pre‐collapse surface displacements, we analyzed Interferometric Synthetic Aperture Radar satellite geodetic data from 2006 to 2018, acquired from ALOS‐1 (2006–2011), COSMO‐SkyMED (2012–2018), and Sentinel‐1 (2014–2018). We identified line‐of‐sight displacements on the southwestern flank throughout the study period. Inversion of COSMO‐SkyMED data revealed a rectangular dislocation with a cumulative slip of 12 m from April 2012 to December 2018. Fixing the fault geometry, we found the optimal slip for time periods corresponding to slip rate changes, ranging from 1.2 to 3.1 m/yr. The slip estimates for ALOS‐1 and Sentinel‐1 data were 0.88 m/yr and 1.1 m/yr, respectively, over their individual time periods. Overall, the detachment fault experienced approximately 15 m of slip from 2006 to 2018 with acceleration and deceleration periods, and a notable acceleration prior to the 2018 collapse.