bluuebunny/crossref_metadata_2025_split huggingface dataset:
Lithostratigraphy, petrography, and geochemistry of the Eocene–Oligocene rock succession, Wadi Arhab area, North Eastern Desert, Egypt
Authors: Amin Alaa Taha Ahmed , El-Mezayen Ahmed Mohamed , Elkholy Darweesh Mohamed , Zeid Ehab Korany Abu , Fahmy Wael , Khamis Hossam Anwar 
Published: March 2024

AbstractThe present study recorded for the first time, an occurrence of Oligocene basaltic plugs in Beni-Suef Governorate, Egypt. In addition, Egyptian alabaster was discovered in the study area, further the well-known ancient area around the Cavern of Wadi Sannur. In this work, the lithostratigraphy, structure, petrography, and geochemistry including TGA and DSC were studied. In addition, the thermal effect of the basaltic magma on the Eocene carbonates that covered most of the area, caused the formation of the Egyptian alabaster in the region. The Eocene succession represents different stages in age (late Middle–Late Eocene). Their outcrops are disconformably overlain by the basaltic plugs extruded along the NW–SE fault trend which is parallel to the Red Sea graben which was formed in the Oligo–Miocene Period. Petrographically, the Eocene rocks show ten microfacies types indicating that deposition of Eocene carbonates in the subtidal shallow marine environment except for microfacies of the Egyptian alabaster shows that the precipitation is related to the basaltic thermal effect and related hydrothermal waters within open cavities or karstic systems in the Eocene carbonate. The chemical analysis showed that SiO2, Al2O3, MgO, Fe2O3, and CaO are of high content as major oxides, while Sr and Ba are of high content in the basaltic plugs as trace elements. TG analysis shows that basaltic rocks in the present study are suitable for conserving high-temperature thermal energy because they do not exhibit any thermal events when exposed to thermal influences by DSC between 30 °C and 1000 °C. Unlike both Egyptian alabaster and recrystallized limestone which are not suitable for maintaining high temperatures between 695 °C and 965 °C and decompose into CaO and CO2 due to the decarbonization of the calcite mineral.
Investigation of Rupture Risk of Thoracic Aortic Aneurysms via Fluid–Structure Interaction and Artificial Intelligence Method
Authors: Koru Murat , Canbolat Gökhan , Darıcık Fatih , Karahan Oguz , Etli Mustafa , Korkmaz Ergün 
Published: March 2024

AbstractPatient-specific studies on vascular flows have significantly increased for hemodynamics due to the need for different observation techniques in clinical practice. In this study, we investigate aortic aneurysms in terms of deformation, stress, and rupture risk. The effect of Ascending Aortic Diameter (AAD) was investigated in different aortic arches (19.81 mm, 42.94 mm, and 48.01 mm) via Computational Fluid Dynamics (CFD), Two-way coupling Fluid–Structure Interactions (FSI) and deep learning. The non-newtonian Carreau viscosity model was utilized with patient-specific velocity waveform. Deformations, Wall Shear Stresses (WSSs), von Mises stress, and rupture risk were presented by safety factors. Results show that the WSS distribution is distinctly higher in rigid cases than the elastic cases. Although WSS values rise with the increase in AAD, aneurysm regions indicate low WSS values in both rigid and elastic artery solutions. For the given AADs, the deformations are 2.75 mm, 6. 82 mm, and 8.48 mm and Equivalent von Mises stresses are 0.16 MPa, 0.46 MPa, and 0.53 MPa. When the rupture risk was evaluated for the arteries, the results showed that the aneurysm with AAD of 48.01 mm poses a risk up to three times more than AAD of 19.81 mm. In addition, an Artificial neural network (ANN) method was developed to predict the rupture risk with a 98.6% accurate prediction by numerical data. As a result, FSI could indicate more accurately the level of rupture risk than the rigid artery assumptions to guide the clinical assessments and deep learning methods could decrease the computational costs according to CFD and FSI.
Ethical consumption and food recovery hierarchy behaviors: a clustering analysis in Japan
Authors: Morais Ana Catarina , Ishida Akira 
Published: March 2024

AbstractConsumption and waste disposal decisions can have a profound impact on the environment. The present research is focused on understanding the adoption profiles of pro-environmental behaviors (PEB) within the Japanese population. The scope of the evaluated PEB covered the purchase, usage, and disposal of food products. Ethical consumption behaviors (the purchase of little packaging, organic, eco-friendly and/or refillable goods), food waste prevention actions (leftovers avoidance, meal planning, and food waste reduction/avoidance), and alternative disposal measures (feeding animals, composting, giving food to neighbors) were analyzed. The profiles were found via a two-step clustering technique using self-reported data from a survey done in nine Japanese prefectures. The first step of the clustering algorithm comprised a hierarchical technique, and the second was a K-means with an Euclidean distance. Four significantly distinct clusters were found, namely: sporadic adopters, average practitioners, responsible consumers, and environmentally involved. Approximately 51% of the sample was allocated to the responsible consumers cluster, which was characterized by having more women in part-time jobs or in-house duties and older respondents than the other groups. Environmentally involved tended to be young respondents with children and gardening/farming interest. They were also the ones who ate plant-based meals more frequently and had healthier eating habits.
Sentiment Analysis in the Age of Generative AI
Authors: Krugmann Jan Ole , Hartmann Jochen 
Published: March 2024

Abstract
In the rapidly advancing age of Generative AI, Large Language Models (LLMs) such as ChatGPT stand at the forefront of disrupting marketing practice and research. This paper presents a comprehensive exploration of LLMs’ proficiency in sentiment analysis, a core task in marketing research for understanding consumer emotions, opinions, and perceptions. We benchmark the performance of three state-of-the-art LLMs, i.e., GPT-3.5, GPT-4, and Llama 2, against established, high-performing transfer learning models. Despite their zero-shot nature, our research reveals that LLMs can not only compete with but in some cases also surpass traditional transfer learning methods in terms of sentiment classification accuracy. We investigate the influence of textual data characteristics and analytical procedures on classification accuracy, shedding light on how data origin, text complexity, and prompting techniques impact LLM performance. We find that linguistic features such as the presence of lengthy, content-laden words improve classification performance, while other features such as single-sentence reviews and less structured social media text documents reduce performance. Further, we explore the explainability of sentiment classifications generated by LLMs. The findings indicate that LLMs, especially Llama 2, offer remarkable classification explanations, highlighting their advanced human-like reasoning capabilities. Collectively, this paper enriches the current understanding of sentiment analysis, providing valuable insights and guidance for the selection of suitable methods by marketing researchers and practitioners in the age of Generative AI.
Family as a Pathway to Suicidal Behaviors Through Depression Symptoms and Internalized Homophobia
Authors: Boyd Donte T. , Sterrett-Hong Emma , Scott Edward D. , Allen Junior L. , Smith Brianna , Quinn Camille R. 
Published: March 2024

AbstractResearch consistently highlights how systemic and social factors can adversely impact mental health, and the potential buffering effects of family support, yet raced sexual minorities are vastly underrepresented among these studies. As rates of suicide increase among Black people and remain high among men and those in gender and sexually diverse communities, this study sought to examine to relationships between family dynamics and suicidality among young Black men who have sex with men (MSM) in young adulthood. We used an online survey to conduct a logistic regression to examine family factors (family support, open family communication, other adult support, and other adult value), depression symptoms, and internalized homophobia on suicide attempts. The conceptualization of the study’s design and interpretation of the results were informed by minority stress theory and the phenomenological variant of ecological systems theory. The results indicate that higher levels of family support and open family communication were associated with lower levels of suicidality. Implications for future research and applications for healthcare providers and human services professionals who support young Black MSM in emerging adulthood are discussed.
“Migrants know better”: migrants’ networks and FDI
Authors: Giovannetti Giorgia , Santi Filippo , Velucchi Margherita 
Published: March 2024

AbstractThe interrelations between trade, FDI and migrations have an important impact on the global economy that resulted in the golden age of globalization at first and in a process of slowbalization in the last decade. Against this background, this paper focuses on migrations and FDI and shows that migrant networks encourage cross-border investments. The presence of immigrants is likely to attract new FDI from their country of origin. FDI abroad, furthermore, are positively related to the presence of migrants, thanks to their knowledge of the two markets. We apply a multilevel mixed model to disentangle the hierarchical structure of the data in order to test the relations between FDI and (directional) migration flows. More specifically we test how and to what extent the structure of the international migrants’ network contributes to bilateral FDI flows, besides standard models. Results show that migrants’ networks exploit the information migrants have on both source and destination markets, and that a more diverse migrants’ community in investing countries lowers bilateral FDI.
Water resource management and flood mitigation: hybrid decomposition EMD-ANN model study under climate change
Authors: Ahmad Nisar , Yi Xu , Tayyab Muhammad , Zafar Muhammad Hamza , Akhtar Naureen 
Published: March 2024

AbstractThe growing population and the rise in urbanization have made managing water a critical concern around the world in recent years. Globally, flooding is one of the most devastating natural disasters. Flood risk mitigation relies heavily on accurate and consistent streamflow forecasts. Pakistan Upper Indus Basin (UIB) is most vulnerable to flooding. Floods have become more frequent in recent decades. UIB can be divided into sub-regions due to its landscape variability, and its collective impact is most prominent in the Massam region. UIB hydrological and meteorological station observations have been used to study seasonal hydro-meteorological variations. To predict flooding, this study proposes a hybrid model combining artificial neural networks as multi-layer perceptron (MLPs) in feed-forward mode, along with empirical mode decomposition (EMD). Data collected by the surface-water hydrology project and Pakistan Meteorological Department from 1960 to 2012, 1969 to 2012, and 1972 to 2012 have been utilized from 17 locations. Statistical parameters and Nash–Sutcliffe Efficiency were measured to analyze the model’s prowess. As a result, decomposition-based models perform better than AI-based models when it comes to prediction accuracy. MLPQTP-EMD performed exceptionally better than competing AI models. The results are further validated by performing a peak value analysis during the flooding season (June–September) achieving a remarkable 91.3% score adding a 5.6% increase by EMD for input data achieving 39.3–32.3% statistical indices scores.
Swimming against the Tide: A Mixed-Methods Study of how the MARKERS Educator Wellbeing Program Changed Educators’ Relational Space
Authors: Cann Rachel , Sinnema Claire , Daly Alan J. , Rodway Joelle 
Published: March 2024

AbstractEffective educator wellbeing interventions should consider the individual, relational, and contextual influences on educator wellbeing. Given the gap between the effectiveness of positive psychology interventions (PPIs) and their real-world success, it is essential to understand and adapt to the school context when integrating psychological interventions into educational settings. The MARKERS (Multiple Action Responsive Kit for Educator, Relational, and School wellbeing) educator wellbeing program is multi-level, designed to consider the individual, relational, and contextual influences on wellbeing. Its multi-foci design also allowed for adaptations to specific contexts. This study examines the impact of the MARKERS program in one school in Aotearoa New Zealand. We use a mixed methods case study approach that draws on measures of educator wellbeing, social network measures of energising interactions, and focus group data. The use of stochastic actor-oriented models (SAOMs) allowed us to examine changes to the social network over time. Findings show that MARKERS program participants experienced a significant positive change in their relational space and experienced more energising interactions, but they were ‘swimming against the tide’ as other staff in the school had fewer energising interactions with their colleagues. Our study illustrates the importance of considering the relational and contextual influences on wellbeing when evaluating educator wellbeing interventions.
Mathematics for optimal design of sustainable infrastructures
Authors: Vázquez-Méndez Miguel E. , Alvarez-Vázquez Lino J. , García-Chan Néstor , Martínez Aurea , Rodríguez Carmen 
Published: March 2024

AbstractThe central aim of this paper is to show that mathematical sciences—in particular a smart combination of modeling, numerical simulation and optimization—can be seen as a useful tool in the design of sustainable infrastructures. Thus, in the first part of this work, we clarify what we mean by systems optimization and suggest how to apply these techniques to improve sustainability. Then, in the second part, we present three interesting environmental problems—all of them involving sustainable infrastructures—that we have studied in the last decade and fit well within this framework: (a) the design of a river fishway to help migratory fish to climb a dam, (b) the design of an irrigation channel to minimize sedimentation and erosion, and (c) the management of an urban road network from an environmental perspective. Based on our experiences, we believe that this innovative use of non-trivial mathematical tools is destined to play a leading role in achieving the United Nations Sustainable Development Goals by helping stakeholders develop the best scientifically informed strategies.
Predicting the antenna properties of helicon plasma thrusters using machine learning techniques
Authors: Malm Oscar , Souhair Nabil , Rossi Alessandro , Magarotto Mirko , Ponti Fabrizio 
Published: March 2024

AbstractWhen designing helicon plasma thrusters, one important characteristic is the impedance of the radio-frequency antenna that is used to deposit power into the plasma. This impedance can be characterized both experimentally and numerically. Recently, a numerical tool capable of predicting the antenna impedance, called Adamant, has been developed. However, Adamant takes a long time to run and has high computer resource demands. Therefore, this work has been done to evaluate whether machine learning models, trained on Adamant-generated data, can be used instead of Adamant for small design change evaluations and similar works. Six different machine learning models were implemented in MATLAB: decision trees, ensembles, support vector machines, Gaussian process regressions, generalized additive models and artificial neural networks. These were trained and evaluated using nested k-fold cross-validation with the hyperparameters selected using Bayesian optimization. The performance target was to have less than 5% error on a point-to-point basis. The artificial neural network performed the best when taking into account both maximum error magnitudes and generalization ability, with a maximum error of 3.98% on the test set and with considerably better performance than the other models when tested on some practical examples. Future work should look into different solver algorithms for the artificial neural network to see if the results could be improved even further. To expand the model’s usefulness it might also be worth looking into implementing different antenna types that are of interest for helicon plasma thrusters.
Constitutional Symbolism in the Shadow of the Common Good
Authors: Duke George 
Published: March 2024

AbstractThis paper aims to clarify the concept of the symbolic constitution and to explain one of its most significant functions: the representation of political unity in complex societies. Section B briefly outlines the concept of the symbolic constitution which informs the arguments of the paper. The next two sections proceed “hermeneutically” through critical engagements with (i) Martin Loughlin’s recent analysis of the symbolic constitution within an ideology-critique of neo-liberal constitutionalism (ii) Niklas Luhmann’s account of the role of symbolic constitutionalism in concealing the function of the modern constitution as a structural coupling between the political and legal sub-systems. Section E then considers the relationship between the symbolic constitution and an alternative “traditional” concept for the representation of political unity: the common good. I argue that the symbolic constitution is both (i) a placeholder which speaks to the abiding relevance of the common good (ii) a symptom of the decline of its preconditions.
Are Odd Radio Circles phoenixes of powerful radio galaxies?
Authors: Shabala S. S. , Yates-Jones P. M. , Jerrim L. A. , Turner R. J. , Krause M. G. H. , Norris R. P. , Koribalski B. S. , Filipović M. , Rudnick L. , Power C. , Crocker R. M. 
Published: March 2024

Abstract
Odd Radio Circles (ORCs) are a class of low surface brightness, circular objects approximately one arcminute in diameter. ORCs were recently discovered in the Australian Square Kilometre Array Pathfinder (ASKAP) data and subsequently confirmed with follow-up observations on other instruments, yet their origins remain uncertain. In this paper, we suggest that ORCs could be remnant lobes of powerful radio galaxies, re-energised by the passage of a shock. Using relativistic hydrodynamic simulations with synchrotron emission calculated in post-processing, we show that buoyant evolution of remnant radio lobes is alone too slow to produce the observed ORC morphology. However, the passage of a shock can produce both filled and edge-brightnened ORC-like morphologies for a wide variety of shock and observing orientations. Circular ORCs are predicted to have host galaxies near the geometric centre of the radio emission, consistent with observations of these objects. Significantly offset hosts are possible for elliptical ORCs, potentially causing challenges for accurate host galaxy identification. Observed ORC number counts are broadly consistent with a paradigm in which moderately powerful radio galaxies are their progenitors.
A novel tactile sensor with multimodal vision and tactile units for multifunctional robot interaction
Authors: Xiong Pengwen , Huang Yuxuan , Yin Yifan , Zhang Yu , Song Aiguo 
Published: March 2024

AbstractRobots with multi-sensors always have a problem of weak pairing among different modals of the collected information produced by multi-sensors, which leads to a bad perception performance during robot interaction. To solve this problem, this paper proposes a Force Vision Sight (FVSight) sensor, which utilizes a distributed flexible tactile sensing array integrated with a vision unit. This innovative approach aims to enhance the overall perceptual capabilities for object recognition. The core idea is using one perceptual layer to trigger both tactile images and force-tactile arrays. It allows the two heterogeneous tactile modal information to be consistent in the temporal and spatial dimensions, thus solving the problem of weak pairing between visual and tactile data. Two experiments are specially designed, namely object classification and slip detection. A dataset containing 27 objects with deep presses and shallow presses is collected for classification, and then 20 slip experiments on three objects are conducted. The determination of slip and stationary state is accurately obtained by covariance operation on the tactile data. The experimental results show the reliability of generated multimodal data and the effectiveness of our proposed FVSight sensor.
Large monochromatic components in expansive hypergraphs
Authors: Bal Deepak , DeBiasio Louis 
Published: March 2024

AbstractA result of Gyárfás [12] exactly determines the size of a largest monochromatic component in an arbitrary
$r$
-colouring of the complete
$k$
-uniform hypergraph
$K_n^k$
when
$k\geq 2$
and
$k\in \{r-1,r\}$
. We prove a result which says that if one replaces
$K_n^k$
in Gyárfás’ theorem by any ‘expansive’
$k$
-uniform hypergraph on
$n$
vertices (that is, a
$k$
-uniform hypergraph
$G$
on
$n$
vertices in which
$e(V_1, \ldots, V_k)\gt 0$
for all disjoint sets
$V_1, \ldots, V_k\subseteq V(G)$
with
$|V_i|\gt \alpha$
for all
$i\in [k]$
), then one gets a largest monochromatic component of essentially the same size (within a small error term depending on
$r$
and
$\alpha$
). As corollaries we recover a number of known results about large monochromatic components in random hypergraphs and random Steiner triple systems, often with drastically improved bounds on the error terms.Gyárfás’ result is equivalent to the dual problem of determining the smallest possible maximum degree of an arbitrary
$r$
-partite
$r$
-uniform hypergraph
$H$
with
$n$
edges in which every set of
$k$
edges has a common intersection. In this language, our result says that if one replaces the condition that every set of
$k$
edges has a common intersection with the condition that for every collection of
$k$
disjoint sets
$E_1, \ldots, E_k\subseteq E(H)$
with
$|E_i|\gt \alpha$
, there exists
$(e_1, \ldots, e_k)\in E_1\times \cdots \times E_k$
such that
$e_1\cap \cdots \cap e_k\neq \emptyset$
, then the smallest possible maximum degree of
$H$
is essentially the same (within a small error term depending on
$r$
and
$\alpha$
). We prove our results in this dual setting.
Sharp bounds for a discrete John’s theorem
Authors: van Hintum Peter , Keevash Peter 
Published: March 2024

AbstractTao and Vu showed that every centrally symmetric convex progression
$C\subset \mathbb{Z}^d$
is contained in a generalized arithmetic progression of size
$d^{O(d^2)} \# C$
. Berg and Henk improved the size bound to
$d^{O(d\log d)} \# C$
. We obtain the bound
$d^{O(d)} \# C$
, which is sharp up to the implied constant and is of the same form as the bound in the continuous setting given by John’s theorem.