bluuebunny/crossref_metadata_2025_split huggingface dataset:
Measurements of Flow Characterization Revealing Transition to Turbulence Associated with the Partial Flexibility-Based Flow Control at Low Reynolds Number
Authors: Koca Kemal , Keskin Sinem , Şahin Rumeysa , Veerasamy Dhamotharan , Genç Mustafa Serdar 
Published: July 2024

AbstractIn order to comprehend the flow characteristics of both controlled and uncontrolled SD7062 wind turbine airfoils with local flexible membrane material throughout a variety of angles of attack at a Reynolds number of 1.05 × 105, an experimental investigation was conducted. The time-dependent force measurement, the hot-wire experiment with a boundary layer and glue-on probes, and the oil-flow visualization technique were all utilized in the present study to measure the flow over the airfoil and examine the laminar-turbulent transition, laminar separation bubble, and the impact of a special flow control method that uses flexibility. A comprehensive intermittency analysis by utilizing hot-wire results was employed to obtain the flow physics effects of the local flexibility the first in the literature. The key results of the experiment demonstrated that the stall was delayed from α = 10° to 12° by the local flexibility. The hot-wire results are dedicated to laminar, transitional and turbulent regions and the transition phenomena at different locations over the suction surface of the airfoil in the analysis graphs. As demonstrated by the results of the oil-flow visualization experiment, in the uncontrolled case, the laminar separation bubble formed over the airfoil at α = 8° between x/c = 0.16 and x/c = 0.42. The use of flexible membrane material over the airfoil provided that the oscillation of this material triggered the transition to turbulence and a bypass transition, which resulted in the reattached flow.
Erenumab escalation in migraine - double dose without additional benefit - a retrospective experience
Authors: Heintz Simon , Storch Peter , Burow Philipp , Maier Patricia , Obermann Mark , Stoessel Grit , Kraya Torsten , Naegel Steffen 
Published: July 2024

Abstract
Background
Erenumab is a monoclonal antibody specifically targeting the CGRP-receptor. Several studies showed efficacy and safety in patients with migraine. Less is known regarding dosage increase, especially in a difficult to treat patients. The aim of the study is to evaluate the increased dosage under real world conditions with particular focus on 70 mg non-responders.

Methods
In a retrospective analysis, patients treated in tertiary headache centers (Halle or Jena, Germany) receiving 70 mg erenumab for at least 3 months with a dosage increase to 140 mg were analyzed. Data were evaluated regarding headache days, intake of acute medication, previous prophylaxis, and medication overuse. Baseline and all treatment intervals were determined as three-month periods.

Results
Datasets of 52 migraine patients (90.4% women) aged between 22 and 78 years (mean 50.4 years, SD 12.1 years) were analyzed. At baseline (mean headache-days 15.67 ± 6.37) 51.9% met criteria for chronic migraine and 56% were currently overusing acute medication. While therapy with 70 mg showed significant improvement in headache days and 50% response, further improvement was not achieved for therapy escalation to 140 mg. The same applies to the secondary endpoints and covers the entire study population as well as the subgroups of chronic and episodic migraine. The 50% response of the 70 mg non-responders for escalation was only 5.14%.

Conclusions
In this difficult-to-treat patient cohort we reconfirmed the effectiveness of erenumab, but could not detect any additional benefit for a dosage escalation from 70 mg to 140 mg erenumab.
Predictive analysis of Metaverse usage intention in the Spanish University
Authors: Castro-Lopez Adrian , Cervero Antonio , Alvarez-Blanco Lucia 
Published: July 2024

AbstractIn technologically advanced societies, the Metaverse has become a three-dimensional digital space that merges the real and virtual worlds, creating new scenarios and possibilities for social interaction. Considering the early stage of its development, the goal of this research is to provide knowledge on the will and intention of university students to use the “Metaverse” for educational purposes, identifying the relevant influential factors on the matter. This would contribute to the assessment and possible action of higher education institutions regarding the integration of this technology in the design of teaching–learning activities. For this purpose, an ex post facto research methodology with a prospective design has been used, relying on a survey conducted with a specially designed questionnaire based on the extended version of the Unified Theory of Acceptance and Use of Technology, covering a sample of 480 university students. Descriptive analysis and structural equation modeling were performed using SPSS and SmartPLS. The influence intention to use the Metaverse were reduced to three dimensions: performance behavior, social influence, and perceived value. The obtained results consolidate, on a theoretical level, the robustness of the Technology Acceptance Model (UTAUT-2). From a practical point of view, they serve to inform tool developers about the aspects to be prioritized and strengthened for implementation of such technological instruments in a teaching–learning context. Therefore, the study provides relevant information about the integration of the Metaverse in educational institutions, especially at the university level, focusing attention on universities as necessary active protagonists of such process.
PPSO and Bayesian game for intrusion detection in WSN from a macro perspective
Authors: Liu Ning , Liu Shangkun , Zheng Wei-Min 
Published: July 2024

AbstractThe security of wireless sensor networks is a hot topic in current research. Game theory can provide the optimal selection strategy for attackers and defenders in the attack-defense confrontation. Aiming at the problem of poor generality of previous game models, we propose a generalized Bayesian game model to analyze the intrusion detection of nodes in wireless sensor networks. Because it is difficult to solve the Nash equilibrium of the Bayesian game by the traditional method, a parallel particle swarm optimization is proposed to solve the Nash equilibrium of the Bayesian game and analyze the optimal action of the defender. The simulation results show the superiority of the parallel particle swarm optimization compared with other heuristic algorithms. This algorithm is proved to be effective in finding optimal defense strategy. The influence of the detection rate and false alarm rate of nodes on the profit of defender is analyzed by simulation experiments. Simulation experiments show that the profit of defender decreases as false alarm rate increases and decreases as detection rate decreases. Using heuristic algorithm to solve Nash equilibrium of Bayesian game provides a new method for the research of attack-defense confrontation. Predicting the actions of attacker and defender through the game model can provide ideas for the defender to take active defense.
Incomplete multi-view partial multi-label classification via deep semantic structure preservation
Authors: Li Chaoran , Wu Xiyin , Peng Pai , Zhang Zhuhong , Lu Xiaohuan 
Published: July 2024

AbstractRecent advances in multi-view multi-label learning are often hampered by the prevalent challenges of incomplete views and missing labels, common in real-world data due to uncertainties in data collection and manual annotation. These challenges restrict the capacity of the model to fully utilize the diverse semantic information of each sample, posing significant barriers to effective learning. Despite substantial scholarly efforts, many existing methods inadequately capture the depth of semantic information, focusing primarily on shallow feature extractions that fail to maintain semantic consistency. To address these shortcomings, we propose a novel Deep semantic structure-preserving (SSP) model that effectively tackles both incomplete views and missing labels. SSP innovatively incorporates a graph constraint learning (GCL) scheme to ensure the preservation of semantic structure throughout the feature extraction process across different views. Additionally, the SSP integrates a pseudo-labeling self-paced learning (PSL) strategy to address the often-overlooked issue of missing labels, enhancing the classification accuracy while preserving the distribution structure of data. The SSP model creates a unified framework that synergistically employs GCL and PSL to maintain the integrity of semantic structural information during both feature extraction and classification phases. Extensive evaluations across five real datasets demonstrate that the SSP method outperforms existing approaches, including lrMMC, MVL-IV, MvEL, iMSF, iMvWL, NAIML, and DD-IMvMLC-net. It effectively mitigates the impacts of data incompleteness and enhances semantic representation fidelity.
Study on the fluidity of the pore-fracture binary system in a tight sandstone reservoir-NMR
Authors: Ge Jing , Zhao Wanchun , Wang Sheng , Hu Song , Chen Guohui 
Published: July 2024

AbstractFluid movability in tight sands may not be accurately characterized by pore size-based classification methods solely because of the complex pore structure and heterogeneity in pore size. In this study, on the basis of casting thin slices and scanning electron microscope observation, pore structure was analyzed using mercury injection, NMR, and micron CT to classify and evaluate the tight oil reservoir. The experiment suggest that the quality of tight reservoir is determined by its pore structure, particularly the throat radius, with the microthroat being an essential factor in permeability. Uniquely, we divide the reservoir by Q-cluster with throat radius, displacement pressure, permeability and other parameters. Based on reservoir classification, this study proposed a method for studying the pore size classification of samples on the T2 spectrum by combining CT scanning with mercury intrusion and a NMR experiment. Pore fluids are generally classified into movable fluid and irreducible fluid by one or two NMR T2 cut-offs. The pore size distributions and capillarity boundaries are converted from T2 and mercury injection capillary pressure (MICP). We categorized pores into micropores (T2 < 1), macropores (T2 > 10, with T2 > 300 as fractures), and medium pores (the rest). The saturation of movable fluid and the percentage of micro-fractures can characterize the seepage characteristics of tight reservoirs, which is of great significance for the later periods of oilfield development.
Dynamic Evolution of Concrete Strength Grades: Insights Across Different Ages and Strain Rates
Authors: Wang Shiming , Bai Yunfan , Shen Yan , Zhou Jian , Khandelwal Manoj 
Published: July 2024

AbstractThis paper investigated the dynamic response of plain concrete with three different strength grades, namely C30, C40, and C50, at an early age (3, 7, 14, and 28 days, respectively). Significant patterns were uncovered using a 75-mm-diameter split Hopkinson pressure bar (SHPB) apparatus. Initially, pronounced viscoelastic behaviour was observed in the concrete’s early stages, characterised by a sharp stress-strain curve both before and after reaching its peak. As the concrete matures, stress concentration within the stress-strain curve becomes more pronounced. Additionally, exponential growth in dynamic strength with higher strain rates was observed, while the strain rate index decreased with age. Improving concrete quality was found to reduce the sensitivity of dynamic strength to strain rate. A viscoelastic damage constitutive model was formulated based on experimental analysis to describe the mechanical response effectively. The evolution of concrete properties over time was accurately captured by fitting model parameters to the experimental data. The theoretical stress-strain curves derived from this damage model closely matched experimental curves across various ages.
Robust estimation for kernel exponential families with smoothed total variation distances
Authors: Kanamori Takafumi , Yokoyama Kodai , Kawashima Takayuki 
Published: July 2024

AbstractIn statistical inference, we commonly assume that samples are independent and identically distributed from a probability distribution included in a pre-specified statistical model. However, such an assumption is often violated in practice. Even an unexpected extreme sample called an outlier can significantly impact classical estimators. Robust statistics studies how to construct reliable statistical methods that efficiently work even when the ideal assumption is violated. Recently, some works revealed that robust estimators such as Tukey’s median are well approximated by the generative adversarial net (GAN), a popular learning method for complex generative models using neural networks. GAN is regarded as a learning method using integral probability metrics (IPM), which is a discrepancy measure for probability distributions. In most theoretical analyses of Tukey’s median and its GAN-based approximation, however, the Gaussian or elliptical distribution is assumed as the statistical model. In this paper, we explore the application of GAN-like estimators to a general class of statistical models. As the statistical model, we consider the kernel exponential family that includes both finite and infinite-dimensional models. To construct a robust estimator, we propose the smoothed total variation (STV) distance as a class of IPMs. Then, we theoretically investigate the robustness properties of the STV-based estimators. Our analysis reveals that the STV-based estimator is robust against the distribution contamination for the kernel exponential family. Furthermore, we analyze the prediction accuracy of a Monte Carlo approximation method, which circumvents the computational difficulty of the normalization constant.
Soil bacterial communities affected by land-use types in a small catchment area of the Balaton Uplands (Hungary)
Authors: Borsodi Andrea K. , Megyes Melinda , Zsigmond Tibor , Horel Ágota 
Published: July 2024

AbstractChanges resulting from different tillage practices can affect the structure of microbial communities, thereby altering soil ecosystems and their functioning. The aim of this study was to explore and compare the physical, chemical properties and bacterial community composition of soils from different land use types (forest, grassland, vineyard, and arable field) in a small catchment. 16S rRNA gene-based amplicon sequencing was used to reveal the taxonomic diversity of summer and autumn soil samples taken from two different slope positions. The greater the anthropogenic impact was on the type of land use, the greater the change was in soil physical and chemical parameters. All sample types were dominated by the phyla Pseudomonadota, Acidobacteriota, Actinobacteriota, Bacteroidota and Verrucomicrobiota. Differences in the relative abundance of various bacterial taxa reflected the different land use types, the seasonality, and the topography. These diversity changes were consistent with the differences in soil properties.
Graphical abstract
Towards Robust Colour Texture Analysis with Limited Training Data
Authors: Shumska Mariya , Wilkinson Michael H. F. , Bunte Kerstin 
Published: July 2024

AbstractTexture analysis plays an important role in different domains of healthcare, agriculture, and industry, where multi-channel sensors are gaining more attention. This contribution presents an interpretable and efficient framework for texture classification and segmentation that exploits colour or channel information and does not require much data to produce accurate results. This makes such a framework well-suited for medical applications and resource-limited hardware. Our approach builds upon a distance-based generalized matrix learning vector quantization (GMLVQ) algorithm. We extend it with parametrized angle-based dissimilarity and introduce a special matrix format for multi-channel images. Classification accuracy evaluation of various model designs was performed on VisTex and ALOT data, and the segmentation application was demonstrated on an agricultural data set. Our extension of parametrized angle dissimilarity measure leads to better model generalization and robustness against varying lighting conditions than its Euclidean counterpart. The proposed matrix format for multichannel images enhances classification accuracy while reducing the number of parameters. Regarding segmentation, our method shows promising results, provided with a small class-imbalanced training data set. Proposed methodology achieves higher accuracy than prior work benchmarks and a small-scale CNN while maintaining a significantly lower parameter count. Notably, it is interpretable and accurate in scenarios where limited and unbalanced training data are available.
Convergence of power sequences of operators via their stability
Authors: Jabłoński Zenon Jan , Jung Il Bong , Kubrusly Carlos , Stochel Jan 
Published: July 2024

AbstractThis paper is concerned with the convergence of power sequences and stability of Hilbert space operators, where “convergence” and “stability” are considered with respect to weak, strong and norm topologies. It is proved that an operator has a convergent power sequence if and only if it is a (not necessarily orthogonal) direct sum of an identity operator and a stable operator. This reduces the issue of convergence of the power sequence of an operator T to the study of stability of T. The question of when the limit of the power sequence is an orthogonal projection is investigated. Among operators sharing this property are hyponormal and contractive ones. In particular, a hyponormal or a contractive operator with no identity part is stable if and only if its power sequence is convergent. In turn, a unitary operator has a weakly convergent power sequence if and only if its singular-continuous part is weakly stable and its singular-discrete part is the identity. Characterizations of the convergence of power sequences and stability of subnormal operators are given in terms of semispectral measures.
Mitigating ecological footprint in BRICS countries: unveiling the role of disaggregated clean energy, green technology innovation and political stability
Authors: Behera Biswanath , Behera Puspanjali , Sucharita Sanhita , Sethi Narayan 
Published: July 2024

AbstractBRICS nations as emerging economies have undergone a substantial structural transformation; however, achieving economic growth while sustaining the ecology is a pressing challenge for these economies. Nonetheless, one of the possible ways to achieve ecological sustainability is to promote clean energy use and green innovation backed by a strengthened political system. Thus, this study assesses the effect of disaggregated clean energy use (hydro and nuclear energy), green technology innovation, and political stability on the ecological footprint in BRICS countries from 1993 to 2022 while controlling for the impacts of natural resources rent, population growth, and GDP per capita. Using dynamic fixed effect and CS-ARDL estimators, the outcomes reveal that hydro and nuclear energy use and political stability induce the ecological footprint in BRICS countries. However, the empirical findings indicate that green technology innovation substantially reduces the ecological footprint. Therefore, from a policy perspective, the study encourages policy coordination among BRICS countries to embrace targeted policies to strengthen regulatory frameworks, promote innovations in technological know-how, and encourage the utilization of clean and sustainable energy to foster environmental sustainability.
Graphical Abstract
Study of performance evaluation of various characteristics of a single phase full bridge inverter circuit using surrounded channel junctionless field effect transistor
Authors: Das Namita , Sarma Kaushik Chandra Deva 
Published: July 2024

AbstractThis paper reports the characteristics study of a single phase full bridge power electronic inverter circuit with a new type of technology namely surrounded channel junctionless field effect transistor (SCJLFET) as a switch. The inverter circuit has been designed using mixed mode simulation platform of TCAD device simulator. The transient characteristics for various temperatures and work functions have been analysed. In the steady state analysis the output current and voltage variation with input voltage with various temperatures and work functions have been studied. The power factor variations with gate oxide thickness and dielectric constant of gate dielectric. The simulation and designing process along with pros and cons have been characterized. The SCJLFET would be one of the strongest participants for advanced power electronics technology as a switches, with various virtues of high-speed operation, low power consumption and low budget process integration. It has also been found that single phase full bridge inverter is giving best results when made using SCJLFET.
Calibration of CFOSAT Off‐Nadir SWIM SWH Product Based on CNN‐LSTM Model
Authors: Zhang Rui , Qi Jinpeng , Yan Qiushuang , Fan Chenqing , Yang Yuchao , Zhang Jie , Wan Yong 
Published: July 2024

AbstractHigh‐precision observation of significant wave height (SWH) is crucial for marine research. The Surface Waves Investigation and Monitoring (SWIM) aboard the China France Oceanography Satellite (CFOSAT) provides the ocean wave spectrum that allows for the calculation of the off‐nadir SWH parameters, but there exists a certain bias with the in‐situ SWH values. To improve the accuracy of the SWH calculation bias from the off‐nadir 6°, 8°, 10° wave spectra and the whole combined spectrum, this paper establishes a spatio‐temporal hybrid model that combines convolutional neural network (CNN) and long short‐term memory network (LSTM). Additionally, to further correct bias exhibited under high sea state, we introduce a bias correction module based on deep neural network (DNN) to adjust the SWIM off‐nadir SWH greater than 4 m. The experimental results demonstrate a significant enhancement in the accuracy of corrected SWIM off‐nadir SWH, and the best calibration result is 10° with 0.267 m root mean square error (RMSE), and 0.979 correlation coefficient (R) compared with the ERA5 value. We conducted a comprehensive study and analysis on the performance of the proposed model under different wave heights, extreme sea states, and wind and swell regions. Meanwhile, the buoy and altimeters are leveraged to render further evaluation the RMSE of the corrected SWH is less than 0.5 m.