bluuebunny/crossref_metadata_2025_split huggingface dataset:
In silico exploration of natural xanthone derivatives as potential inhibitors of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) replication and cellular entry
Authors: Obakachi Vincent A. , Nchiozem-Ngnitedem Vaderament-A. , Govender Krishna K. , Govender Penny P. 
Published: February 2025

Abstract
The COVID-19 pandemic, caused by SARS-CoV-2, has underscored the urgent need for effective antiviral therapies, particularly against vaccine-resistant variants. This study investigates natural xanthone derivatives as potential inhibitors of the ACE2 receptor, a critical entry point for the virus. We computationally evaluated 91 xanthone compounds derived from Swertia chirayita, identifying two promising candidates: 8-O-[β-D-Xylopyranosyl-(1→6)-β-D-glucopyranosyl]-1,7-dihydroxy-3-methoxy xanthone (XAN71) and 8-O-[β-D-Xylopyranosyl-(1→6)-β-D-glucopyranosyl]-1-hydroxy-3,7-dimethoxy-xanthone (XAN72). Molecular docking and dynamics simulations (MDDS) were performed to assess their binding energy and stability within the ACE2 active site, comparing them to the reference inhibitor MLN-4067. The top six compounds were selected based on their docking performance, followed by Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) calculations to quantify binding affinities. Additionally, molecular electrostatic potential (MEP) analysis was conducted to visualize electron density regions relevant to binding interactions. Our results demonstrate that XAN71 and XAN72 exhibit superior binding affinities of -70.97 and − 69.85 kcal/mol, respectively, outperforming MLN-4067 (-61.33 kcal/mol). MD simulations revealed stable interactions with key ACE2 residues, primarily through hydrogen bonds and hydrophobic contacts. The Molecular Electrostatic Potential(MEP) analysis further elucidated critical electron density regions that enhance binding stability. This study establishes XAN71 and XAN72 as viable candidates for ACE2 inhibition, providing a structural basis for their development as natural xanthone-based therapeutics against SARS-CoV-2. These findings highlight the potential of targeting ACE2 with natural compounds to combat COVID-19, particularly in light of emerging viral variants.
Relative Effects on Life Satisfaction Revisited: Social Comparison is Only Half the Story
Authors: Hirata Johannes , Groß Patricia 
Published: February 2025

Abstract
Empirical happiness research clearly shows that life satisfaction depends, among other things, on relative effects. However, even where the data do not permit any inferences regarding the mechanisms that give rise to relative effects, studies often claim that relative effects are the result of comparisons that individuals make between their situation and that of others. We argue that this interpretation is not always justified because it disregards existing alternative interpretations with strong empirical support and a long tradition in the literature. To structure the interpretation of relative effects, we propose a model that distinguishes between the relative effects that influence aspirations and those that influence capabilities (i.e., the objective opportunities available to a person). We show where a number of relative effects fall into place in this model and we discuss the significance of our model for life satisfaction research.
Gender Equality and Life Satisfaction: A Mediation Model with Individual Autonomy, Income Per Capita and Trust
Authors: Graafland Johan , Schilpzand Annemiek 
Published: February 2025

Abstract
Gender equality has been found to positively affect life satisfaction. However, the reason why gender equality affects life satisfaction remains relatively unexplored. In this paper, we hypothesize three mediators for this relationship: individual autonomy, income per capita, and generalized trust. All three variables have been found to positively affect life satisfaction. We argue that each mediator may, in turn, depend positively on gender equality, suggesting that individual autonomy, income per capita, and generalized trust positively mediate the relationship between gender equality and life satisfaction. Using a sample of 81 countries from 1990 to 2020, we find that individual autonomy and income per capita are important channels that together explain 98% of the total relationship between gender equality and life satisfaction. While the mediation effect of individual autonomy is robust, the significance of income per capita is less consistent when using alternative estimation techniques. For generalized trust we do not find evidence of mediation.
Comparing tree stress rank and tree condition to determine red oak (Quercus spp.) health in Greentree Reservoirs in the lower Mississippi Alluvial Valley
Authors: Hug Cassandra , Saud Pradip , McKnight Keight , Askren Ryan J. , Osborne Douglas 
Published: February 2025

Abstract
Individual tree health plays a vital role in maintaining a forest’s ecological functions, including resources for waterfowl and other wildlife. Seasonal flooding due to altered hydrology is a major stressor on individual tree health in Greentree reservoirs (GTR), impounded bottomland hardwood forests especially less water tolerant species like red oaks (Quercus spp.). We evaluated the health of individual red oak species (n = 6,432) in 662 plots across elevation gradients in 12 GTRs within the lower Mississippi Alluvial Valley using two tree health assessment approaches. The first approach assigns tree conditions (i.e., stressed, moderate, low) based on overall qualitative tree attributes, while the second approach ranks stress, assigning numerical value based on the severity of four distinct qualitative tree attributes (i.e., tip dieback, epicormics branch, bark condition, basal swell). The result indicated that the highest mean stress rank and the highest proportion of stressed tree conditions were red oak species, nuttall oak (Q. texana; 18.59, 0.44), willow oak (Q. phellos; 18.66, 0.38) and cherrybark oak (Q. pagoda; 18.90, 0.37). Red oak stress is positively correlated to elevation across the landscape (τ = 0.10, p < 0.001), but is negatively correlated to relative elevation, topographical changes, within each GTR (τ =  − 0.11, p < 0.001). Additionally, the two health assessments are significantly associated (χ
2 = 313.78, df = 2, p < 0.001) and had a 13.1% misclassification rate. By utilizing the stress rank method for better classification of tree conditions to understand the adverse effect of prolonged flooding on the health of desirable red oak and other native tree species, management practices can be adjusted to improve tree health in GTRs, benefiting both wildlife and economic value.
Integrating quantum computing into building-to-grid control framework: Application of benders decomposition in mixed-integer nonlinear programming
Authors: Deng Zhipeng , Wang Xuezheng , Dong Bing 
Published: February 2025

Abstract
Buildings use a large amount of energy in the United States. It is important to optimally manage and coordinate the resources across building and power distribution networks to improve overall efficiency. Optimizing the power grid with discrete variables was very challenging for traditional computers and algorithms, as it is an NP-hard problem. In this study, we developed a new optimization solution based on quantum computing for BTG integration. We first used MPC for building loads connected with a commercial distribution grid for cost reduction. Then we used discretization and Benders Decomposition methods to reformulate the problem and decompose the continuous and discrete variables, respectively. We used D-Wave quantum computer to solve dual problems and used conventional algorithm for primal problems. We applied the proposed method to an IEEE 9-bus network with 3 commercial buildings and over 300 residential buildings to evaluate the feasibility and effectiveness. Compared with traditional optimization methods, we obtained similar solutions with some fluctuations and improved computational speed from hours to seconds. The time of quantum computing was greatly reduced to less than 1% of traditional optimization algorithm and software such as MATLAB. Quantum computing has proved the potential to solve large-scale discrete optimization problems for urban energy systems.
Deep Learning Innovations in the Detection of Lung Cancer: Advances, Trends, and Open Challenges
Authors: Liz-López Helena , de Sojo-Hernández Áurea Anguera , D’Antonio-Maceiras Sergio , Díaz-Martínez Miguel Angel , Camacho David 
Published: February 2025

Abstract
Cancer is the second leading cause of death worldwide, and within this type of disease, lung cancer is the second most diagnosed, but the leading cause of death. Early detection is crucial to increase patient survival rates. One of the primary methods for detecting this disease is through medical imaging, which, due to its features, is well-suited for analysis by deep learning techniques. These techniques have demonstrated exceptional results in similar tasks. Therefore, this paper focusses on analyzing the latest work related to lung cancer detection using deep learning, providing a clear overview of the state of the art and the most common research directions pursued by researchers. We have reviewed DL techniques for lung cancer detection between 2018 and 2023, analyzing the different datasets that have been used in this domain and providing an analysis between the different investigations. In this state-of-the-art review, we describe the main datasets used in this field and the primary deep learning techniques used to detect radiological signs, predominantly convolutional neural networks (CNNs). As the impact of these systems in medicine can pose risks to patients, we also examine the extent to which explainable AI techniques have been applied to enhance the understanding of these systems, a crucial aspect for their real-world application. Finally, we will discuss the trends that the domain is expected to follow in the coming years and the challenges that researchers will need to address.
Automatic prediction of stroke treatment outcomes: latest advances and perspectives
Authors: Samak Zeynel A. , Clatworthy Philip , Mirmehdi Majid 
Published: February 2025

Abstract
Stroke is a major global health problem that causes mortality and morbidity. Predicting the outcomes of stroke intervention can facilitate clinical decision-making and improve patient care. Engaging and developing deep learning techniques can help to analyse large and diverse medical data, including brain scans, medical reports, and other sensor information, such as EEG, ECG, EMG, and so on. Despite the common data standardisation challenge within the medical image analysis domain, the future of deep learning in stroke outcome prediction lies in using multimodal information, including final infarct data, to achieve better prediction of long-term functional outcomes. This article provides a broad review of recent advances and applications of deep learning in the prediction of stroke outcomes, including (i) the data and models used, (ii) the prediction tasks and measures of success, (iii) the current challenges and limitations, and (iv) future directions and potential benefits. This comprehensive review aims to provide researchers, clinicians, and policy makers with an up-to-date understanding of this rapidly evolving and promising field.
The role of the ethics expert in Spanish legislation on euthanasia and mental health
Authors: Ramos-Pozón Sergio 
Published: February 2025

Abstract
This article examines the assessment of mental capacity in the context of euthanasia, particularly when requested by patients with mental illnesses. It proposes a holistic alternative approach to the traditional functional model, arguing that the latter is insufficient to capture the complexity of these patients’ decisions. Using approaches based on narrative, hermeneutic, and dialogical ethics, it offers an evaluation that considers the patient’s life story, values, and context. Shared decision-making and empathy are identified as fundamental components to ensure informed and consensual decisions, promoting an environment of respect and mutual understanding. The article reviews Spanish legislation on euthanasia, highlighting the need to include medical ethics experts in the Guarantee and Evaluation Commissions. These experts provide a comprehensive ethical perspective essential for addressing the ethical complexities in euthanasia requests and ensuring fair decisions that reflect the patient’s true will. It recommends reviewing and expanding current protocols, as well as including continuous ethics training to improve medical practice in this context. The conclusions suggest that an assessment of mental capacity based on ethical principles and an integrated narrative can significantly improve medical practice and decision-making in euthanasia, especially for these patients. Furthermore, the inclusion of ethics experts in the commissions can provide a more humane and just perspective, ensuring that decisions respect the patient’s dignity and autonomy.
Absorption–Reflection–Transmission Power Coefficient Guiding Gradient Distribution of Magnetic MXene in Layered Composites for Electromagnetic Wave Absorption
Authors: Zhou Yang , Zhang Wen , Pan Dong , Li Zhaoyang , Zhou Bing , Huang Ming , Mi Liwei , Liu Chuntai , Feng Yuezhan , Shen Changyu 
Published: February 2025

Abstract
The morphological distribution of absorbent in composites is equally important with absorbents for the overall electromagnetic properties, but it is often ignored. Herein, a comprehensive consideration including electromagnetic component regulation, layered arrangement structure, and gradient concentration distribution was used to optimize impedance matching and enhance electromagnetic loss. On the microscale, the incorporation of magnetic Ni nanoparticles into MXene nanosheets (Ni@MXene) endows suitable intrinsic permittivity and permeability. On the macroscale, the layered arrangement of Ni@MXene increases the effective interaction area with electromagnetic waves, inducing multiple reflection/scattering effects. On this basis, according to the analysis of absorption, reflection, and transmission (A–R–T) power coefficients of layered composites, the gradient concentration distribution was constructed to realize the impedance matching at low-concentration surface layer, electromagnetic loss at middle concentration interlayer and microwave reflection at high-concentration bottom layer. Consequently, the layered gradient composite (LG5-10–15) achieves complete absorption coverage of X-band at thickness of 2.00–2.20 mm with RL
min of −68.67 dB at 9.85 GHz in 2.05 mm, which is 199.0%, 12.6%, and 50.6% higher than non-layered, layered and layered descending gradient composites, respectively. Therefore, this work confirms the importance of layered gradient structure in improving absorption performance and broadens the design of high-performance microwave absorption materials.
Moderating effects of energy poverty for sustainable tourism, policy, innovation, and environmental resilience: evidence from SEM-ANN approaches
Authors: Neger Meher , Rahid Abu Obida , Alnour Mohammed 
Published: February 2025

Abstract
The global tourism sector's rapid growth has exerted significant pressure on environmental resources and ecosystems, underscoring the urgent need for sustainable solutions. While previous studies have explored the roles of sustainable tourism practices, effective tourism policies, and technological innovations in enhancing environmental resilience, there is a notable gap in understanding how energy poverty influences these dynamics. This study addresses this gap by examining the moderating role of reduced energy poverty in strengthening the relationships between Sustainable Tourism (ST), Tourism Policy Effectiveness (TPE), Technological Innovations (TI), and Environmental Resilience (ER). Utilizing data from 378 participants, including tourism stakeholders, policymakers, researchers, and environmental specialists in Bangladesh, this research employs a multi-method approach, integrating Structural Equation Modeling (PLS-SEM) and Artificial Neural Networks (ANN) for robust analysis. The findings reveal that ST, TPE, TI, and Less Energy Poverty (LEP) significantly enhance environmental resilience, with LEP acting as a critical moderator that amplifies the positive impacts of these factors. By highlighting energy poverty as a pivotal barrier and a potential enabler, this study contributes to the global discourse on sustainable tourism and environmental management, emphasizing the transformative potential of improved energy access. This research provides a unique global perspective by demonstrating how reducing energy poverty not only fosters environmental resilience in tourism destinations but also serves as a scalable solution for other developing regions. The integration of SEM and ANN further enhances the methodological rigor and ensures the reliability of the findings. As the first study to explore the moderating role of energy poverty in the nexus of sustainable tourism and environmental resilience, this research offers valuable insights for policymakers, practitioners, and academics, advocating for global strategies to enhance energy access as a catalyst for sustainable environmental outcomes.
Hospital-onset bacteremia in the neonatal intensive care unit: strategies for risk adjustment
Authors: Prochaska Erica C. , Xiao Shaoming , Colantuoni Elizabeth , Elhaissouni Nora , Clark Reese H. , Johnson Julia , Mukhopadhyay Sagori , Kalu Ibukunoluwa C. , Zerr Danielle M. , Reich Patrick J. , Roberts Jessica , Flannery Dustin D. , Milstone Aaron M. ,   Group Information: CDC Prevention Epicenters Program
Published: February 2025

Abstract

Objective:
To quantify the impact of patient- and unit-level risk adjustment on infant hospital-onset bacteremia (HOB) standardized infection ratio (SIR) ranking.

Design:
A retrospective, multicenter cohort study.

Setting and participants:
Infants admitted to 284 neonatal intensive care units (NICUs) in the United States between 2016 and 2021.

Methods:
Expected HOB rates and SIRs were calculated using four adjustment strategies: birthweight (model 1), birthweight and postnatal age (model 2), birthweight and NICU complexity (model 3), and birthweight, postnatal age, and NICU complexity (model 4). Sites were ranked according to the unadjusted HOB rate, and these rankings were compared to rankings based on the four adjusted SIR models.

Results:
Compared to unadjusted HOB rate ranking (smallest to largest), the number and proportion of NICUs that left the fourth quartile (worst-performing) following adjustments were as follows: adjusted for birthweight (16, 22.5%), birthweight and postnatal age (19, 26.8%), birthweight and NICU complexity (22, 31.0%), birthweight, postnatal age and NICU complexity (23, 32.4%). Comparing NICUs that moved into the better-performing quartiles after birthweight adjustment to those that remained in the better-performing quartiles regardless of adjustment, the median percentage of low birthweight infants was 17.1% (Interquartile Range (IQR): 15.8, 19.2) vs 8.7% (IQR: 4.8, 12.6); and the median percentage of infants who died was 2.2% (IQR: 1.8, 3.1) vs 0.5% (IQR: 0.01, 12.0), respectively.

Conclusion:
Adjusting for patient and unit-level complexity moved one-third of NICUs in the worst-performing quartile into a better-performing quartile. Risk adjustment may allow for a more accurate comparison across units with varying levels of patient acuity and complexity.
Semi-parametric estimation of system reliability for multicomponent stress-strength model under hierarchical Archimedean copulas
Authors: Wang Junrui , Yan Rongfang 
Published: February 2025

Abstract
In the reliability analysis of multicomponent stress-strength models, it is typically assumed that strengths are either independent or dependent on a common stress factor. However, this assumption may not hold true in certain scenarios. Therefore, accurately estimating the reliability of the stress-strength model becomes a significant concern when strengths exhibit interdependence with both each other and the common stress factor. To address this issue, we propose an Archimedean copula (AC)-based hierarchical dependence approach to effectively model these interdependencies. We employ four distinct semi-parametric methods to comprehensively estimate the reliability of the multicomponent stress-strength model and determine associated dependence parameters. Furthermore, we derive asymptotic properties of our estimator and demonstrate its effectiveness through both Monte Carlo simulations and real-life datasets. The main original contribution of this study is the first attempt to evaluate the reliability problem under dependent strengths and stress using a hierarchical AC approach.
Seasonal El Niño‐Southern Oscillation and Indian Ocean Dipole Dependencies of Methane Emissions From Wetlands
Authors: Zhu Tingting , Zhou Yanlian , Ju Weimin 
Published: February 2025

AbstractWetlands are a significant natural source of methane (CH4), and their emissions are highly sensitive to climatic variability. This study investigated the impact of El Niño‐Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) events on wetland CH4 emission anomalies based on a model simulation data set from 1980 to 2022. Results showed El Niño reduced CH4 emission anomalies, whereas La Niña increased them. Both ENSO and IOD phases greatly altered climatic variables, which in turn affected CH4 emission anomalies. The global mean sensitivities of CH4 emission anomalies to ENSO and IOD were −0.17 and −0.08 mg CH4 m−2 month−1, respectively, which exhibited pronounced temporal and spatial variability, mainly driven by soil moisture and temperature. This study underscored the pronounced temporal and spatial variability of CH4 emission anomalies under ENSO and IOD phases, which would improve the accuracy of predictive CH4 models and inform strategies for mitigating climate change.
A GNSS‐Velocity Clustering Method Applicable at Local to Global Scales
Authors: Takahashi Atsushi , Yano Keisuke , Kano Masayuki 
Published: February 2025

AbstractWe propose a hierarchical clustering methodology for clustering data from a global navigation satellite system (GNSS) that is applicable at local to global scales. We first adapted the conventional 2D velocity clustering metric for global‐scale applications by implementing parallel translation in differential geometry. We then combined it with a Euler‐vector‐based metric to incorporate the kinematic constraint associated with the rigid motion of plates, achieving advantages in identifying tectonic structures. This hybrid metric approach is assessed through two case studies at different spatial scales to determine whether it can accurately identify tectonic plate and crustal block boundaries: one study uses global‐scale data from the ITRF2008 plate motion model, and the other focuses on a local‐scale study in Taiwan. Results obtained using the hybrid metric consistently align better with geological data than those from either the 2D or Euler vector‐based metrics alone. The proposed method is computationally efficient, enabling us to conduct two types of stability assessment: examination of the robustness of clusters with synthetic noise contamination and leave‐one‐out analysis. Both tests are demonstrated to be feasible within practical timeframes.