bluuebunny/crossref_metadata_2025_split huggingface dataset:
A nonadditive rough set model for long-term clinical efficacy evaluation of chronic diseases in real-world settings
Authors: Xiaoli Chu , Juan Xu , Xiaodong Chu , Bingzhen Sun , Yan Zhang , Kun Bao , Yanlin Li 
Published: February 2024

AbstractThe pursuit of clinical effectiveness in real-world settings is at the core of clinical practice progression. In this study, we address a long-term clinical efficacy evaluation decision-making problem with temporal correlation hybrid attribute characteristics. To address this problem, we propose a novel approach that combines a temporal correlation feature rough set model with machine learning techniques and nonadditive measures. Our proposed approach involves several steps. First, over the framework of granular computing, we construct a temporal correlation hybrid information system, the gradient method is employed to characterize the temporal attributes and the similarity between objects is measured using cosine similarity. Second, based on the similarity of gradient and cosine, we construct a composite binary relation of temporal correlation hybrid information, enabling effective classification of this information. Third, we develop a rough set decision model based on the Choquet integral, which describes temporal correlation decision process. We provide the ranking results of decision schemes with temporal correlation features. To demonstrate the practical applications of our approach, we conduct empirical research using an unlabeled dataset consisting of 3094 patients with chronic renal failure (CRF) and 80,139 EHRs from various clinical encounters. These findings offer valuable support for clinical decision-making. Two main innovations are obtained from this study. First, it establishes general theoretical principles and decision-making methods for temporal correlation and hybrid rough sets. Second, it integrates data-driven clinical decision paradigms with traditional medical research paradigms, laying the groundwork for exploring the feasibility of data-driven clinical decision-making in the field.
Transformers in health: a systematic review on architectures for longitudinal data analysis
Authors: Siebra Clauirton A. , Kurpicz-Briki Mascha , Wac Katarzyna 
Published: February 2024

AbstractTransformers are state-of-the-art technology to support diverse Natural Language Processing (NLP) tasks, such as language translation and word/sentence predictions. The main advantage of transformers is their ability to obtain high accuracies when processing long sequences since they avoid the vanishing gradient problem and use the attention mechanism to maintain the focus on the information that matters. These features are fostering the use of transformers in other domains beyond NLP. This paper employs a systematic protocol to identify and analyze studies that propose new transformers’ architectures for processing longitudinal health datasets, which are often dense, and specifically focused on physiological, symptoms, functioning, and other daily life data. Our analysis considered 21 of 456 initial papers, collecting evidence to characterize how recent studies modified or extended these architectures to handle longitudinal multifeatured health representations or provide better ways to generate outcomes. Our findings suggest, for example, that the main efforts are focused on methods to integrate multiple vocabularies, encode input data, and represent temporal notions among longitudinal dependencies. We comprehensively discuss these and other findings, addressing major issues that are still open to efficiently deploy transformers architectures for longitudinal multifeatured healthcare data analysis.
A comprehensive survey of artificial intelligence-based techniques for performance enhancement of solid oxide fuel cells: Test cases with debates
Authors: Ashraf Hossam , Draz Abdelmonem 
Published: February 2024

AbstractSince installing solid oxide fuel cells (SOFCs)-based systems suffers from high expenses, accurate and reliable modeling is heavily demanded to detect any design issue prior to the system establishment. However, such mathematical models comprise certain unknowns that should be properly estimated to effectively describe the actual operation of SOFCs. Accordingly, due to their recent promising achievements, a tremendous number of metaheuristic optimizers (MHOs) have been utilized to handle this task. Hence, this effort targets providing a novel thorough review of the most recent MHOs applied to define the ungiven parameters of SOFCs stacks. Specifically, among over 300 attempts, only 175 articles are reported, where thirty up-to-date MHOs from the last five years are comprehensively illustrated. Particularly, the discussed MHOs are classified according to their behavior into; evolutionary-based, physics-based, swarm-based, and nature-based algorithms. Each is touched with a brief of their inspiration, features, merits, and demerits, along with their results in SOFC parameters determination. Furthermore, an overall platform is constructed where the reader can easily investigate each algorithm individually in terms of its governing factors, besides, the simulation circumstances related to the studied SOFC test cases. Over and above, numerical simulations are also introduced for commercial SOFCs’ stacks to evaluate the proposed MHOs-based methodology. Moreover, the mathematical formulation of various assessment criteria is systematically presented. After all, some perspectives and observations are provided in the conclusion to pave the way for further analyses and innovations.
Against the tide: how changes in political alignment affect grant allocation to municipalities in Hungary
Authors: Vasvári Tamás , Longauer Dóra 
Published: February 2024

AbstractThe 2019 local election in Hungary accelerated the trend of centralization, marked by a simultaneous reduction in available local funds and an increase in discretionary transfers to local governments. This paper, utilizing a dataset encompassing all over 3000 municipalities from 2015 to 2020, employs fixed-effect estimations and a regression discontinuity design to explore how election outcomes influenced central decisions on intergovernmental transfers. Generally, larger municipalities are more susceptible to political influence, particularly in the allocation of discretionary grants, whereas smaller settlements appear less affected by political shifts. Changes in political alignment triggered a rewarding policy for municipalities that remained or converted to aligned status, resulting in an additional 86.4% and 65.2% of discretionary funds, respectively, relative to those converting to or remaining unaligned. Our research establishes that political influence in intergovernmental transfers has intensified since 2019, offering valuable insights for the upcoming 2024 election.
‘Trauma work’ as hindrance to political praxis during democratisation movements
Authors: Al Azmeh Zeina , Baert Patrick 
Published: February 2024

AbstractThis paper examines the impact of a shift in focus from political praxis to trauma work in the context of a failed democratisation movement. It investigates the various phenomena which emerge when intellectuals, under the traumatic impact of violence and atrocities, place trauma narration at the core of their interventions. Drawing on document analysis, participant observation and semi-structured interviews with twenty nine exiled Syrian intellectuals in Paris and Berlin who had participated in the revolutionary movement of 2011, the paper suggests that an inversion of the normative power structures pertaining to how intellectuals relate to their publics occurs when they adopt, under conditions of extreme violence and trauma, what we call a radically embedded positionality vis-à-vis ‘the people’. This results in the dismantling of previous figurations of the ‘militant intellectual’ along with praxis-focused notions of the ‘responsibility of intellectuals’, ultimately undermining their ideational influence upon domestic publics and weakening their political impact and critical role within a revolutionary movement.
System Thinking for Sustainable Water Management: The Use of System Tools in Sustainability Transitions
Authors: Wan Rosely Wan Izar Haizan , Voulvoulis Nikolaos 
Published: February 2024

AbstractManaging water resources to meet current social, economic, and environmental needs without compromising the ability to meet those needs in the future, requires water managers to look beyond jurisdictional boundaries and their immediate supply operations, protect shared water and marine environments, resources and ecosystems from pollution and over-abstraction and deliver structural changes and resilient solutions to make the provision of water services sustainable. With a plethora of system tools increasingly available to support the sustainability transformation of water systems, sorting through their breadth and diversity to determine what is useful and what is not can be challenging. From a selection of 40 popular system tools grouped in terms of how they function, we review those most commonly used in water management through 35 water-related studies from the literature. Findings demonstrate that system tools are not a substitute for systems thinking and applying them alone does not qualify the development of systemic solutions. Systems thinking offers a powerful approach to first improving collective understanding of sustainability challenges related to water and then, in collaboration with a diverse group of stakeholders and the public, defining desired goals for water management and working together to make them a reality. System tools in that context can play an important role in facilitating the process and here we propose an overarching framework of how to select and apply available tools to facilitate a holistic, integrated, and interdisciplinary systems approach for water management and wider urban sustainability transitions.
Anandamide and WIN 55212–2 Afford Protection in Rat Brain Mitochondria in a Toxic Model Induced by 3-Nitropropionic Acid: an In Vitro Study
Authors: Maya-López Marisol , Monsalvo-Maraver Luis Angel , Delgado-Arzate Ana Laura , Olivera-Pérez Carolina I. , El-Hafidi Mohammed , Silva-Palacios Alejandro , Medina-Campos Omar , Pedraza-Chaverri José , Aschner Michael , Tinkov Alexey A. , Túnez Isaac , Retana-Márquez Socorro , Zazueta Cecilia , Santamaría Abel 
Published: February 2024

AbstractMitochondrial dysfunction plays a key role in the development of neurodegenerative disorders. In contrast, the regulation of the endocannabinoid system has been shown to promote neuroprotection in different neurotoxic paradigms. The existence of an active form of the cannabinoid receptor 1 (CB1R) in mitochondrial membranes (mitCB1R), which might exert its effects through the same signaling mechanisms as the cell membrane CB1R, has been shown to regulate mitochondrial activity. Although there is evidence suggesting that some cannabinoids may induce protective effects on isolated mitochondria, substantial evidence on the role of cannabinoids in mitochondria remains to be explored. In this work, we developed a toxic model of mitochondrial dysfunction induced by exposure of brain mitochondria to the succinate dehydrogenase inhibitor 3-nitropropionic acid (3-NP). Mitochondria were also pre-incubated with the endogenous agonist anandamide (AEA) and the synthetic CB1R agonist WIN 55212–2 to evaluate their protective effects. Mitochondrial reduction capacity, reactive oxygen species (ROS) formation, and mitochondrial swelling were assessed as toxic markers. While 3-NP decreased the mitochondrial reduction capacity and augmented mitochondrial ROS formation and swelling, both AEA and WIN 55212–2 ameliorated these toxic effects. To explore the possible involvement of mitCB1R activation on the protective effects of AEA and WIN 55212–2, mitochondria were also pre-incubated in the presence of the selective CB1R antagonist AM281, which completely reverted the protective effects of the cannabinoids to levels similar to those evoked by 3-NP. These results show partial protective effects of cannabinoids, suggesting that mitCB1R activation may be involved in the recovery of compromised mitochondrial activity, related to reduction of ROS formation and further prevention of mitochondrial swelling.
Florida’s fiery subtropical grasslands: Growth forms, belowground organs, and post-fire recovery strategies
Authors: Orzell Steve L. , Bitomský Martin , Bridges Edwin L. , Budach Brett , Klimešová Jitka , Martinková Jana , Reed Zachary E. , Raynor Seth J. 
Published: February 2024

AbstractFire-adaptive traits in plants of tropical and subtropical grassy ecosystems have been the subject of considerable global research, but only recently studied in pyrogenic Florida subtropical grasslands. Plant growth forms, belowground organs, and post-fire recovery strategies were studied for 198 grassland specialists in peninsular Florida. Community types (dry-mesic, mesic, wet-mesic, and wet) were sampled with 1m2 plots along the edaphic-hydrologic gradient and the association between these variables and fire-related plant traits was tested using fourth-corner analysis. Caulescent herbs (43), cespitose graminoids (27), and rosette herbs (31) are the most common growth forms among species sampled. Plants with epigeogenous and hypogeogenous rhizomes dominate the sample plots, including matrix graminoids, shrub geoxyles, and an acaulescent rhizomatous fire-resilient palm (Serenoa repens). Most species (163; 82%) exhibit resprouting, including 30 facultative resprouters and 133 obligate resprouters. All woody rhizomatous species are obligate resprouters, and 35 ephemeral herbaceous species are obligate reseeders. Community type was a better predictor of species abundances than hydrology, however, hydrology was significantly associated with species traits measured, particularly rhizome texture, with woody rhizomes prevalent in all but the wet sites. Belowground organs (xylopodia, geoxylic suffructices) and growth form were associated with frequent fire and phylogeny, suggesting fire regime as a driver of community phylogenetic diversity. Persistence, rapid resilience and co-occurrence of geoxyles align Florida subtropical grasslands with other global geoxyle grasslands. The old-growth, pyrogenic grassy ecosystems of peninsular Florida are the center of geoxyle diversification on the southeastern US coastal plain.
Population Fusion Transformer for Subnational Population Forecasting
Authors: Alghanmi Nusaybah , Alotaibi Reem , Alshammari Sultanah , Mahmood Arif 
Published: February 2024

AbstractForecasting the subnational population accurately is needed for sustainable development, including planning for the future, allocating resources, or providing health services. Two approaches are used for forecasting subnational populations: local forecasting where a model is trained for each area, and global forecasting, where one model is trained with all areas. Local forecasting (e.g., statistical models) is limited to capturing the population growth patterns in a single area. Machine learning models, such as the light gradient boosting model (LGBM), are considered a more suitable approach for global forecasting, but it is limited to one-step predictions, leading to error accumulation. Also, combining several models into one ensemble model are used which helped in reduce forecasting errors. However, the nature of population growth is nonlinear, and there is a need to reduce error accumulation. This study overcomes these issues and proposes a population fusion transformer (PFT) as a global forecasting model for population forecasting, which outputs multi-step predictions. The PFT is based on a temporal fusion transformer (TFT) proposing a novel deep gated residual network (DGRN) block to capture data nonlinearity. This study also incorporates the proposed PFT model into various ensemble models to reduce forecasting errors using different prediction and learning approaches. The proposed models are applied to four subnational population datasets from several countries. The PFT model outperforms the LGBM and TFT with lower forecasting errors in three and two datasets. More importantly, combining the PFT with other models in ensemble models reduced errors further.
Hidden curricula in academic medicine: Streamlining success for early career scholars from majority and diverse backgrounds
Authors: Enders Felicity T. , Golembiewski Elizabeth H. , DSouza Karen N. , Martin Ashley E. , Kennedy Cassie C. 
Published: February 2024

Abstract
The hidden curriculum (HC), or implicit norms and values within a field or institution, affects faculty at all career stages. This study surveyed affiliates of a junior faculty training program (n = 12) to assess the importance of HC topics for junior faculty, mentors, and institutional leaders. For non-diverse junior faculty and their mentors, work-life balance, research logistics, and resilience were key HC topics. Coping with bias and assertive communication were emphasized for diverse junior faculty and mentors. Institutional norms and vision were essential for leaders, while networking was important for all groups. Future research should explore HC needs and potential interventions.
The Media Pluralism Principle, The Financing of Public Broadcasters, and EU Law
Authors: Kozak Małgorzata 
Published: February 2024

AbstractIn the rise of authoritarian trends in Hungary and in Poland, public broadcasters play a crucial role in supporting the political forces in power. There are many examples which show how public broadcasters influence public opinion by selective coverage and distorted remit. While the problem of media plurality is often commented upon from the perspective of fundamental rights or the rule of law, another relevant perspective is that of internal market and economic law. The article analyses how can one make sure that the use of public funds to support State media does not lead to the distortion of citizens’ rights to be informed. The article argues that the current EU State Aid framework allows the balance of Member States’ prerogatives in respect to media freedom and content with the risk of EU-imposed censure. The article analyses the place of media pluralism in EU law and demonstrates that media pluralism has been integrated within the internal market framework, including an analysis of recent amendments to the Audiovisual Media Services Directive. Then, it focuses on the relationship between media pluralism, public service broadcasting and EU State aid law.
Phasing out coal on Svalbard: From a conflict of interest to a contest over symbolic capital
Authors: Vold Hansen Tiril 
Published: February 2024

Abstract
In 2021, the decision to close the last Norwegian coal mine on Svalbard was made, and with that, the Norwegian coal adventure on the archipelago came to an end. This was a result of a political process, which is the focus of this article. Drawing on fieldwork conducted during the fall of 2022, I argue that the political process of phasing out coal changed from a conflict over interests to a contest over symbolic capital. The article contributes to the understanding of Norwegian Svalbard politics and the “balancing act” that this represents. I focus on how power, in the form of shaping people’s perceptions and as prestige, influenced what interests prevail and why. The article addresses (1) why the decision to phase out coal was not made earlier, (2) what ultimately made this decision possible and (3) why and over what the key actors were still competing after the decision to phase out coal was made.
Development of decision rules for an adaptive aftercare intervention based on individual symptom courses for agoraphobia patients
Authors: Wilhelm Maximilian , Moessner Markus , Jost Silke , Okon Eberhard , Malinowski Volker , Schinke Katharina , Sommerfeld Sebastian , Bauer Stephanie 
Published: February 2024

AbstractAs other mental illnesses, agoraphobia is associated with a significant risk for relapse after the end of treatment. Personalized and adaptive approaches appear promising to improve maintenance treatment and aftercare as they acknowledge patients’ varying individual needs with respect to intensity of care over time. Currently, there is a deficit of knowledge about the detailed symptom course after discharge from acute treatment, which is a prerequisite for the empirical development of rules to decide if and when aftercare should be intensified. Therefore, this study aimed firstly at the investigation of the naturalistic symptom course of agoraphobia after discharge from initial treatment and secondly at the development and evaluation of a data-driven algorithm for a digital adaptive aftercare intervention. A total of 56 agoraphobia patients were recruited in 3 hospitals. Following discharge, participants completed a weekly online monitoring assessment for three months. While symptom severity remained stable at the group level, individual courses were highly heterogeneous. Approximately two-thirds of the patients (70%) reported considerable symptoms at some time, indicating a need for medium or high-intense therapeutic support. Simulating the application of the algorithm to the data set resulted in an early (86% before week six) and relatively even allocation of patients to three groups (need for no, medium, and high-intense support respectively). Overall, findings confirm the need for adaptive aftercare strategies in agoraphobia. Digital, adaptive approaches may provide immediate support to patients who experience symptom deterioration and thus promise to contribute to an optimized allocation of therapeutic resources and overall improvement of care.
Fast hybrid methods for modeling landslide susceptibility in Ardal County
Authors: Xu Shangshang 
Published: February 2024

AbstractRecently, machine learning models have received huge attention for environmental risk modeling. One of these applications is landslide susceptibility mapping which is a necessary primary step for dealing with the landslide risk in prone areas. In this study, a conventional machine learning model called multi-layer perceptron (MLP) neural network is built upon advanced optimization algorithms to achieve a firm prediction of landslide susceptibility in Ardal County, West of Iran. The used geospatial dataset consists of fourteen conditioning factors and 170 landslide events. The used optimizers are electromagnetic field optimization (EFO), symbiotic organisms search (SOS), shuffled complex evolution (SCE), and electrostatic discharge algorithm (ESDA) that contribute to tuning MLP’s internal parameters. The competency of the models is evaluated using several statistical methods to provide a comparison among them. It was discovered that the EFO-MLP and SCE-MLP enjoy much quicker training than SOS-MLP and ESDA-MLP. Further, relying on both accuracy and time criteria, the EFO-MLP was found to be the most efficient model (time = 1161 s, AUC = 0.879, MSE = 0.153, and R = 0.657). Hence, the landslide susceptibility map of this model is recommended to be used by authorities to provide real-world protective measures within Ardal County. For helping this, a random forest-based model showed that Elevation, Lithology, and Land Use are the most important factors within the studied area. Lastly, the solution discovered in this study is converted into an equation for convenient landslide susceptibility prediction.