bluuebunny/crossref_metadata_2025_split huggingface dataset:
Reseña. El Abencerraje. Estudio y notas de Eugenia Fosalba, Madrid, Real Academia Española (Biblioteca Clásica, 33), 2017
Authors: Vicente Baldrich Mireia 
Published: February 2025

Esta edición, al cuidado de Eugenia Fosalba, presenta las tres versiones distintas de El Abencerraje en un único volumen, que permite al lector consultar y valorar por sí mismo las cualidades de cada una. La versión más antigua, conocida como Crónica es la que más se aproxima al texto original, de carácter oral, escrita no antes de 1548. Esta versión, muy desdeñada por los estudiosos, dejó un espacio crítico muy útil que la editora ha aprovechado con una edición crítica anotada y justificada dentro de las coordenadas interpretativas modernas. La segunda versión apareció en el Inventario de Antonio de Villegas, y la tercera, la más leída y difundida, se insertó al final del libro IV de la Diana de Montemayor (edición vallisoletana). La editora aborda en el estudio introductorio los problemas irresolubles hasta el momento del Abencerraje: ¿Quién es el autor de la Crónica?, ¿cuál es la evolución textual de las diferentes versiones?, y/o ¿fue Montemayor quién firmó la versión inserta en la Diana? Sobre la autoría de la Crónica, Fosalba apunta a Jerónimo Jiménez de Urrea, hombre de temperamento humanista, militar y escritor, responsable de la famosa traducción del Orlando furioso y la Arcadia, como la más plausible. Acerca de la precedencia de las versiones queda demostrado como la Crónica es anterior a I y D con indicios irrefutables como son las notas eróticas y las palabras malsonantes incluidas en algunos pasajes de C que se tijeretean en I y en D. Finalmente un exhaustivo cotejo de la versión aparecida en la Diana con otras obras de Montemayor avalarían la hipótesis de que el escritor luso está detrás del Abencerraje pastoril.
Quantification of internal heterogeneity across a submarine channel bend: a unique example from the late Tortonian Tachrift Channel Complex 5 (Taza–Guercif Basin, NE Morocco)
Authors: Pantopoulos George , Marini Mattia , Invernizzi Daniele , El Kati Imad , Mcarthur Adam D. , Felletti Fabrizio 
Published: February 2025

ABSTRACT
The heterogeneity of a deep marine slope channel complex is investigated through the acquisition of fifty sedimentary logs across three high-quality exposures of the late Tortonian Tachrift Complex 5, which constitutes part of the Neogene sedimentary infill of the Taza–Guercif Basin, NE Morocco. Various metrics extracted from log data (net-to-gross, amalgamation ratio, facies proportions, etc.) are processed to assess heterogeneity trends across the studied deposits. Sedimentological and stratigraphic analyses indicate that the exposures constitute upstream and downstream parts of a large-scale, left-turning channel bend. Sedimentary facies comprise different types of amalgamated sandstones, heterolithic and mud-rich sediments, grouped in channel-fill and overbank facies associations. Architectural elements recognized in channel-fill deposits are mainly lateral-accretion packages (LAPs), with additional elements recognized and recorded for the first time, representing different depositional settings across the channel bend, such as outer-bank bars and inner- to outer-bank transition bars. Analysis of heterogeneity metrics indicates the occurrence of two main types of LAP, which vary depending on their upstream or downstream position across the bend, each with different internal heterogeneity (e.g., mean and lateral trends of net-to-gross, facies proportions, etc.) when compared with other elements deposited across the channel. The range and spatial arrangement of heterogeneity metrics observed could serve as an analog for assessing heterogeneity across channel bends of similar slope channel fills in the subsurface.
Strength and Deformation Behaviors of Hydrate-Bearing Silty-Clayey Sediments With Different Compaction Patterns
Authors: Zhang Chenyi , Luo Tingting , Han Tao , Zhang Yu , Tian Zhuangcai , Sun Peixin , Xue Yong , Li Yanghui , Yang Weihao , Madhusudhan B. N. 
Published: February 2025

Summary
Clarifying the mechanical response of hydrate-bearing silty-clayey sediments (HBSCSs) concerning various mining factors is crucial for safety and consecutive hydrate production. Comprehension of the impact of compaction patterns on the sediment structure within gas hydrate reservoirs, influenced by the geological age and setting, remains limited in the field. Two types of HBSCS samples were thus prepared, differentiated by the consolidation sequence: the CG sample, consolidated before hydrate formation, and the GC sample, consolidated after hydrate formation, corresponding to compaction patterns in this study, with their mechanical behaviors analyzed considering different influencing factors. The conclusions indicate that an initial shearing dilatancy peak occurs in volumetric strain curves of CG samples with 40% hydrate saturation. In addition, the shearing dilatancy trend of CG samples caused by strong particle exclusion and hydrate accumulation promotes the increase in strength and stiffness as well as the decrease in final volumetric strain of HBSCSs, when compared with GC samples. The frictional property of HBSCSs prepared by the CG method is stronger than that of the GC method due to hydrate displacing sediment particles, although weaker cemented property; there are increases of 9.54° and 0.46 MPa in the internal friction angle and cohesion of CG samples as the hydrate saturation increases from 0 to 40%, while the variation in internal friction angle of GC samples is inconspicuous. The results in this study provide potential theoretical support for predicting the compaction patterns on the mechanical behaviors of hydrate silty-clayey reservoirs.
Prediction of Buildup Rate for an Ultrashort-Radius Well Trajectory Drilled with a Flexible Drilling System: A Semi-Analytical Solution
Authors: Liu Xin , Huang Zhongwei , Wu Xiaoguang , Li Gensheng , Shi Huaizhong , Chen Han , Wang Ge , Peng Yuxuan 
Published: February 2025

Summary
Flexible tool drilling (FTD) technology is an efficient and cost-effective method with a short construction period for sidetracking operations. Unlike conventional drilling pipes, flexible drilling tools consist of a series of short pipes connected by universal joints. The buildup radius ranges between 3 m and 5 m. A more accurate prediction of the buildup rate is necessary to reduce uncertainties in tool design and drilling process set. In this paper, we investigate the influence of structural and mechanical parameters on the buildup rate (BUR) of flexible drilling tools. A semi-analytical model was developed, achieving an accuracy of ±0.3 m after field validation with six FTD branches. The model clarified key relationships among the BUR, load transfer, and trajectory characteristics and analyzed the effects of parameters such as maximum bending angle, pipe length, bit anisotropy, friction, and axial force on the top flexible pipe. The study demonstrates that higher bit anisotropy improves directional control and increases the BUR, although it also raises mechanical loads, particularly side forces. The optimal bit anisotropy index should not be lower than 0.7. Shorter pipe lengths enable sharper directional changes but increase mechanical losses. The optimal parameters are recommended as a bit anisotropy above 0.8, a pipe length of 0.15–0.2 m, an axial force of 4–6 tons, a maximum bending angle of 3°, and a minimized friction factor. Our research also concludes that axial force should be greater than 4 tons to ensure sufficient weight on bit (WOB) and pipe string stability. Furthermore, the friction factor primarily affects mechanical load distribution along the pipes rather than trajectory. This research provides theoretical support for the trajectory control of flexible drilling tools and enables their adaptation to various application scenarios.
What do we actually want to experience? A computational metric for assessing reward values
Authors: Waugh Christian E. , Porth Adam P. , Fang Xuanyu , Sands L. Paul , Kishida Kenneth T. 
Published: February 2025

Abstract

People’s motivation to have different experiences is predicated on how much they find those experiences rewarding or not, and these reward values are not always fully accessible to our consciousness. In two studies, we demonstrate that using a combination of reinforcement learning (RL) paradigms and computational modeling, we can measure computationally inferred reward values (cRV) of experiences, which do not rely on conscious self-report. Consistent with motivational reward theory, convenience samples of participants exhibited higher cRV (greater reward value of that experience) to viewing positive vs. negative images (subject pool; Study 1) and to viewing more vs. less attractive faces (online sample; Study 2). Further, these cRVs were sensitive to context (familiarity vs. novelty of images, Study 1) and to individual differences (attraction preference, Study 2). Lastly, although cRVs were mildly correlated with explicit self-report values, which demonstrates their validity, they were better predictors of behavior than were the explicit values, which suggests that cRVs are capturing reward processes that are not represented by explicit value judgments. This method of measuring cRV holds great promise for understanding the motivation driving people’s choices of a variety of experiences across a wide array of fields of study.
Glucose Deprivation-Mediated Disulfide Stress Induces Cell Death in FHdeficient Renal Cell Carcinoma
Authors: Xu Guowang , Dou Peng , Lu Xin , Geng Pengyu , Cole Yasemin , Bao MIao , Ge Xin , Pu Siming , Zhou Lina , Liu Xinyu , Li Hang , Zhao Jinhui , Chen Fating , Lin Zhikun , Wang Ting , Li Qi , Hu Jianwen , Dai Jie , Qian Xu , Zhuang Zhengping 
Published: February 2025

Abstract

FHdeficient renal cell carcinoma (RCC) is a highly metastatic and challenging malignancy. Here, we show that the aberrant accumulation of intracellular disulfides in FHdeficient cells leads to actin cytoskeleton contraction and induces a caspase-independent form of cell death. However, inhibition of Rho-associated coiled-coil containing protein kinase (ROCK) (which regulates actin cytoskeleton contraction) suppresses FHdeficient cell death. Under glucose starvation, FHdeficient cells attempt to mitigate NADPH consumption by downregulating the reductive tricarboxylic acid (TCA) cycle. However, defects in the oxidative TCA cycle prevent them from maintaining NADPH balance. Moreover, targeting mitochondrial complex I in FHdeficient cells enhances the reductive TCA cycle while weakening the oxidative TCA cycle, thereby accelerating intracellular disulfide accumulation, tumor growth is significantly inhibited under low glucose conditions. Our study uncovers a caspase-independent cell death mechanism driven by disulfide stress and provides a potential therapeutic strategy targeting the metabolic vulnerability of FHdeficient RCC.
Correlation Regularized Image Denoising via Low Rank Matrix Approximation
Authors: özkaramanlı hüseyin 
Published: February 2025

Abstract

In this paper, we propose a new residual correlation based regularization method for image denoising and formulate the problem in the framework of Low Rank Matrix Approximation. For this purpose we propose an objective function which leads naturally to the weighted nuclear norm minimization. The objective function consists of a fidelity term and a regularization term which are the Frobenious norms of residual noise and the deviation of residual noise correlations from the true correlations. Convexity of the objective function is established and the closed form global minimum is obtained. A simple closed form formula for the residual noise singular values is derived. The formula involves the right singular vectors of the noisy observation matrix and the noise correlation matrix. Our solution explicitly shows how each singular value of the noisy observation matrix is thresholded to obtain the clean signal singular values. Hard and soft thresholding as well as the weighted thresholding of singular values come out as solutions of the proposed optimization problem. Simulation results in image denoising show that the proposed method performs as good as the weighted nuclear norm minimization method with better performance for most standard test images especially at high noise levels.
Surface Defect Detection of Magnetic Tiles Based on an Improved Lightweight GhostNet Network
Authors: Shang Xingwang , Li Xueqin , Shi Peng , Zhu Weibing , Jiang Jiayu 
Published: February 2025

Abstract

In modern industry, the performance of permanent magnet motors is crucial to enhancing efficiency and system reliability. As a key component of these motors, the quality of magnetic tiles directly impacts motor performance. Therefore, ensuring that magnetic tile surfaces are free of defects is essential for maintaining product quality. While traditional visual inspection methods have been widely used, they suffer from limitations in precision and stability. Although deep learning has improved detection accuracy, it increases the demand on computational resources. To address these issues, this paper proposes an optimized lightweight deep learning model called S-GhostNet, which aims to enhance the efficiency and accuracy of magnetic tile surface defect detection while reducing computational complexity. S-GhostNet employs advanced optimization techniques, such as generating Ghost features using different dilation rates in convolutional layers to capture multi-scale defect information, thereby enhancing feature diversity. Channel shuffling and depthwise separable convolutions promote feature fusion and reduce redundant computations. Additionally, the integration of Feature Pyramid Networks (FPN) improves the detection of defects of various sizes. Experimental results show that S-GhostNet achieves an accuracy of 95.46\% in magnetic tile surface defect detection, achieving a 14.36\% improvement over the original GhostNet, while reducing computational cost (FLOPs) by approximately 29.76\%, from 4.20 G to 2.95 G. This demonstrates that S-GhostNet not only enhances detection accuracy but also significantly reduces the required computational resources, highlighting its advantages in this field.
Quantization of a Llama Language Model for improved Efficiency and Inference
Authors: Madhanegha S , Vishnuvaradhan V , Arun R , Surenther I 
Published: February 2025

Abstract

Despite their transformational potential, large language models (LLMs) like Llama are difficult to implement on devices with limited computational power due to their high computational requirements. This study explores the quantization of the Lamba model, a method that minimizes memory footprint and model size for effective deployment. In order to obtain significant model compression with acceptable performance, we investigate different quantization techniques. At various quantization levels, the study will assess the trade-off between efficiency and accuracy. We will also look into how quantization affects the target devices' power consumption and inference speed. By enabling deployment on resource-constrained platforms and effectively quantifying the Llama model, this initiative seeks to democratize access to potent AI tools, encouraging greater innovation and practical applications.
Additionally, a smaller model results in cheaper implementation costs and enhanced sustainability due to lower inference power usage.
In order to quantified the Llama model, this research explores a number of technical approaches, assesses performance trade-offs, and optimizes deployment for effective hardware use. This project's objective is to successfully quantify the Llama model in order to show that it is feasible to implement it in contexts with limited resources. The results will help create LLMs that are easier to use and more effective.
Sustainable development in the European Union countries: panel data model evidence
Authors: Bartosiewicz Aleksandra , Szymanska Agata Gniadkowska - , Kucharski Adam 
Published: February 2025

Abstract

Sustainable development (SD) is significant in the European Union (EU) policy. This article examines the impact of key macroeconomic factors on the SD of the logistics enterprises of 25 EU countries divided into four regions. The macroeconomic factors included Gross Domestic Product per capita (GDP per capita), unemployment rate, wages, external balance of goods and services, and Harmonised Index of Consumer Prices (HICP). The methodological part used panel models with fixed effects, ANOVA and the Scheffé procedure. Between 2008 and 2023, different macroeconomic factors influenced the SD depending on the region of Europe. The SD is most strongly affected by a change in the unemployment rate (Western, Central and Eastern Europe) and the HICP (Northern and Southern Europe). The GDP per capita proves significant in all regions except Northern Europe and is the most important variable in panel models for Southern Europe. In two regions (Western, Central and Eastern Europe), individual environmental effects are usually higher than those in the social area. The highest levels of sustainability in the economic domain are observed in panel models for Northern Europe and the lowest - for Southern Europe. The latter is much worse at implementing the SDGs than other European regions.
High-resolution Canada domain disturbance forcings suitable for land surface modeling applications
Authors: Beaver Jason , Curasi Salvatore R. , Melton Joe R. , Humphreys Elyn R. , Hermosilla Txomin , Wulder Michael A. 
Published: February 2025

Abstract

Spatially explicit fire and harvest data are useful for driving land surface model (LSM) simulations of the carbon cycle. From 1985-present, numerous Canadian disturbance datasets exist. However, before the launch of Landsat-4 (1984), few are available. We create spatially explicit LSM disturbance drivers for Canada for 1740-2018. We catalog and harmonize spatial and aspatial datasets and develop a novel algorithm that reconstructs disturbance far back in time using stand age. Based on possible historical scenarios, we reconstruct 283-394 Mha of fire and 3.42 Mha of harvest in total Canada-wide from 1740-1918. After 1918, when spatial records are available, we supplement them by reconstructing 25.79-60.30 Mha of fire and 24.75 Mha of harvest. After 1984, we exclusively use spatially explicit records. We verify the algorithm by comparing the inputs and resultant drivers and examine diagnostic metrics to disentangle the contribution of spatial, aspatial, and stand-age data. Our forcings and algorithm will improve the representation of disturbance-mediated impacts on Canada’s terrestrial carbon cycle and possibly in other regions.
Hypoxia tolerance of intertidal triplefin fish is associated with low critical oxygen tension and high phosphorylating capacity in brain mitochondria
Authors: Devaux Jules B.L. , McArley Tristan J. , Herbert Neill , H Anthony J.R. 
Published: February 2025

Abstract

At the terminus of the O2 cascade, mitochondria play an important role in O2 utilisation and energy conservation, with adaptive modifications occasionally shared among hypoxia-tolerant species. Here, we sought to determine whether mitochondrial adaptations in brain tissue explain the hypoxia tolerance of New Zealand triplefin fishes (Tripterygiidae). We compared two intertidal species (Bellapiscis medius and Forsterygion lapillum), both likely adapted to hypoxia-reoxygenation exposures, and two subtidal species (F. varium and F. malcomi), which inhabit normoxic waters. To assess hypoxia tolerance, we determined loss of equilibrium (LOE) during hypoxia exposure and measured the critical O2 tension (Pcrit). Intertidal species displayed superior hypoxia tolerance as assessed by LOE and also had lower Pcrit (LOE versus Pcrit R2=0.99). High-resolution respirometry was used to measure mitochondrial respiration in homogenate and permeabilised fragments of brain. While a weak relationship was apparent between mitochondrial O2 binding affinity (mP50) and LOE, maximum phosphorylating O2 consumption (OxPhos) and O2 catalytic rates were strongly correlated with hypoxia tolerance. Although cytochrome-c-oxidase activity was highest in the most hypoxia-tolerant species B. medius, it was only weakly correlated with hypoxia tolerance across species. Notably, the high OxPhos capacity of intertidal species was not associated with higher whole animal resting O2 consumption, suggesting intertidal species maintain high capacity for ATP production without incurring increased basal energetic costs. While somewhat paradoxical, the low Pcrit/high OxPhos strategy of intertidal species may provide flexibility in the dynamic intertidal environment where short, severe periods of hypoxia are interspersed with high energy demand environmental conditions (e.g. acute warming).
Real-Time Financial Fraud Detection Using Adaptive Graph Neural Networks and Federated Learning
Authors: Rahmati Milad 
Published: February 2025

Abstract

Detecting financial fraud in real time is an ongoing challenge due to the ever-evolving nature of fraudulent activities. Conventional fraud detection systems rely heavily on static machine learning models, which often struggle to adapt to emerging fraud patterns. Additionally, data privacy regulations and institutional constraints limit collaborative fraud detection efforts, as financial organizations are often unable to share sensitive transactional data. In this research, we introduce a real-time fraud detection framework that combines Adaptive Graph Neural Networks (GNNs) and Federated Learning (FL) to overcome these limitations. The GNN component dynamically models relationships within financial transactions, allowing the system to detect suspicious patterns as they emerge rather than relying on historical fraud markers. Meanwhile, federated learning enables multiple financial institutions to collaboratively train fraud detection models without directly sharing customer data, thus addressing privacy concerns. To enhance explainability and regulatory compliance, the proposed system integrates Explainable AI (XAI) methods, making fraud detection decisions more transparent. Experimental evaluations on benchmark financial datasets and real-world transactional data reveal that our approach improves fraud detection accuracy by 15–30% while reducing false positives compared to existing machine learning-based solutions. The findings highlight the potential of GNNs and FL in advancing fraud prevention strategies while maintaining data security and interpretability, making it a promising alternative to traditional fraud detection mechanisms.