bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Uncertainty Inherent to DEM Simulations: Insights from an Interlocking Parcel Model
Authors: Maier Lukas , Mitterlindner Michael , Benabchiasli Hadie , Fasching Gregor , Radl Stefan 
Published: November 2024

Abstract

In industrial applications, the handling of heterogeneous mixtures of phases and materials poses challenges for direct measurements and experiments, necessitating complementary modeling approaches. The Discrete Element Method (DEM) is commonly used for simulating the flow of granular systems, typically with spherical particles. However, certain applications, such as recycled polymers and batteries, require alternative non-convex particle representations in DEM simulations. Tetrapods are a promising shape candidate for modeling the flow behavior of such materials, as well as the associated uncertainty. We investigate the impact of the tetrapods’ properties on the outcome and uncertainty inherent to DEM-based simulations. We demonstrate that tetrapods are effective for modeling interlocking materials, with their shape and size parameter significantly affecting interlocking behavior. Most interestingly, we can correlate the shape and size of tetrapods to the uncertainty inherent to our simulations. Specifically, we find that this uncertainty is positively correlated with both tetrapod size and the interlocking parameter ξ/D that quantifies their non-convexity. Lastly, we provide guidelines for selecting optimal tetrapod parameter sets for accurately modeling materials based on mean and variability measured in experiments.
Gait Speed as a Superior Screening Indicator for Mild Cognitive Impairment Compared to Walk Ratio and Dual-Task Cost: A Cross-Sectional Study
Authors: Wang Xiaoqin , Wu Jiani , Tian Qi , Liu Xintong , Yu Weihua , Lü Yang 
Published: November 2024

Abstract

Background
The most accurate and sensitive quantitative indicator for screening patients with subjective cognitive decline (SCD) and mild cognitive impairment (MCI) had yet to be established. This study aimed to assess the comparative efficacy of gait speed, walk ratio, and dual-task cost (DTC) in detecting patients with SCD and MCI.
Methods
Our study involved the measurement and comparison of clinical features and gait indicators among 96 patients with MCI, 66 patients with SCD, and 50 individuals with normal cognition (NC). The correlation analysis, receiver operating characteristic curves (ROCs), and binary logistic regression analysis were utilized to investigate the relationship between gait indicators, SCD, and MCI.
Results
The female patients exhibited a greater susceptibility to SCD and MCI (p < 0.001). Significant differences in gait speed, walk ratio, and DTC were observed between NC and MCI group, as well as between SCD and MCI group (all p < 0.05). However, no significant differences were identified between NC and SCD group. After adjusting for gender, age, education level, Body mass index (BMI), and Mini-mental State Examination (MMSE) scores, a significant correlation was observed between gait speed and the risk of developing MCI. Importantly, the ROC curve showed that the AUC of dual speed is the highest at 0.7662 [95% CI (0.6935,0.8388)]. The AUCs of single speed, single walk ratio, dual walk ratio, and DTC were 0.7333, 0.6027, 0.6609, and 0.5907, respectively. Notably, the DTC had no predictive ability (p = 0.55).
Conclusions
The gait speed, walk ratio, and DTC could identify MCI but were not effective in identifying SCD. Furthermore, gait speed emerged as the most accurate and sensitive indicator for identifying individuals with MCI when compared to walk ratio and DTC.
Interaction between TUG1 and METTL3 dynamically regulates liver cancer cell self-renewal
Authors: Hou Ping , Wu Rongshou , Luo Juan , Deng Jianyong , Zhang Rongguiyi , Liu Weiwei , Wang Jiakun , Wu Linquan , Li Enliang 
Published: November 2024

Abstract

Background
The long non-coding TUG1 regulates the mRNA stability of target genes by acting as a competing endogenous RNA in the cytoplasm. However, its function in the nucleus and the underlying mechanism are unknown. We examined the potential interaction between TUG1 and METTL3 and the underlying molecular mechanism in liver cancer cells.
Methods
The expressions of TUG1 and METTL3 in hepatocellular carcinoma (HCC) tissues and liver cancer cell lines were examined by quantitative RT-PCR, western blotting, and fluorescence in situ hybridization. Loss of function experiments were used to examine the role of TUG1 and METTL3 in HCC. A liver cancer tissue microarray was used to identify the influence of METTL3 on prognosis and clinical outcomes. In vitro analyses, whole transcriptome sequencing, RNA sequencing, and database analyses were performed to investigate the molecular mechanism of TUG1 and METTL3 in liver cancer.
Results
TUG1 and METTL3 were localized in the nucleus of liver cancer cells. Knockdown of TUG1 and METTL3 decreased the proliferative and migration ability of liver cancer cells in vitro, and METTL3 knockdown promoted tumorigenicity in vivo. High METTL3 expression correlated with unfavorable prognosis of HCC patients. Mechanistic studies revealed that TUG1 regulates METTL3 transcriptional expression by binding and recruiting EZH2 to the METTL3 promoter and increasing H3K27me3 levels. TUG1 is regulated by METTL3 in a m6A-YTHDC1-dependent manner. Knockdown of METTL3 substantially abolished the m6A level of TUG1 and augmented TUG1 expression. METTL3 and EZH2 proteins may indirectly interact through the “bridge” of TUG1. The interaction between TUG1 and METTL3 may play a role in liver cancer self-renewal.
Conclusion:
TUG1 may epigenetically repress METTL3 transcription in liver cancer cells by binding and recruiting EZH2 to the METTL3 promoter region, resulting in increased H3K27me3 levels. METTL3 regulates TUG1 transcription in an m6A-dependent manner. The interaction between METTL3 and TUG1 dynamically regulates liver cancer cell self-renewal activity.
Exploring the Impact of Emotional Intelligence and Self-Efficacy on Academic Achievementamong Adolescents in Injibara Sub-City, Northwest Ethiopia
Authors: fetene muluneh 
Published: November 2024

Abstract

Emotional intelligence refers to the ability to recognize the importance of emotions, understand their connections, and use this understanding to analyze and solve problems. Selfefficacy beliefs, as defined by Bandura, are one’s internal convictions about their ability to perform tasks effectively. This study explores the relationship between emotional intelligence, academic self-efficacy beliefs, and academic success among high school students. Using a crosssectional design, data were collected from 200 students across two of three senior secondary schools. The sample was selected through purposive and random sampling, comprising students from four grade levels (Grades 9–12).Students’ stress management skills were evaluated using a stress management scale with a Cronbach's alpha of 0.77. Emotional intelligence was assessed using the Schutte Self-Report Emotional Intelligence (SSEI) Test, which consists of 33 items rated on a five-point Likert scale, achieving a reliability score of α = 0.88. One-way ANOVA and multiple regression analyses were employed to examine the association between emotional intelligence, self-efficacy, and academic achievement. The results indicated that late adolescents had a significantly higher mean score (M = 146.26) compared to middle (M = 132.94) and early adolescents (M = 119.79), with F = 98, df = 2, p<0.05. Findings revealed that emotional intelligence and self-efficacy beliefs together accounted for 63% of the variance in academic achievement. Thus, it can be concluded that students' self-efficacy beliefs and emotional intelligence significantly contribute to academic success.
The impact of genotype transformations and aneuploidy on genomic prediction accuracy in polyploid species: a simulation study
Authors: Chen Chensong , Ross Elizabeth , Dinglasan Eric , Powell Owen , Hayes Ben 
Published: November 2024

Abstract

The accuracy of genomic prediction for key traits in sugarcane remains low relative to other crops, even with growing reference sets. This is potentially due to high-level polyploidy, frequent aneuploidy and complex genetic architectures. Here we aim to assess the impact of genotype transformations typically used in polyploid crops on the accuracy of genomic prediction. We focus on the effect of using “diploid” genotype calls and aneuploidy. We simulated a sugarcane genome with multi-species ancestry and genome duplications, resulting in 96 chromosomes. We included a range of allele dosage to phenotype maps across ploidies including additive linear, dominant, non-linear sigmoid and log shaped maps. Genomic prediction accuracies were compared from six algorithms including genomic BLUP (GBLUP), extended GBLUP, BayesR, Multilayer perceptron, Convolutional neural network and Attention network. Accuracy of prediction for phenotypes controlled by a higher proportion of additive effects were more sensitive to diploid transformation and aneuploidy events. Conversely, accuracy of prediction for phenotypes involving directional dominance were less affected by diploid transformation but were still substantially affected by aneuploidy. For traits controlled by non-linear allele dosage to phenotype maps, the predictive accuracy was significantly affected by diploid transformation, but the influence of aneuploidy was not obvious. Extended GBLUP performed well and gave higher accuracies than other linear based models across most of scenarios, while the DL models were more competitive when the allele action across ploidies became more complex. Overall, our results demonstrate that genotypes that capture allele dosage should be used in genomic prediction for complex polyploids.
GRBMTI: A Multi-Feature Fusion Approach Combining GraRep and RNA2vec for MiRNA-MRNA Interaction Prediction
Authors: Shi Tai-Long , Wong Leon , Wang Lei , You Zhu-Hong , Yu Chang-Qing , Jiang Chen , Liang Si-Zhe 
Published: November 2024

Abstract

MicroRNA (miRNA) interactions with messenger RNA (mRNA) are critical in biological processes, and predicting these interactions is crucial for understanding their mechanisms. Given the limitations of traditional biological experimental methods, developing appropriate predictive models to generate high-quality potential targets has become increasingly prevalent in this field. However, current predictive methods are problematic because they rely solely on potential miRNA target sites instead of utilizing the entire mRNA sequence, leading to potential feature loss. Considering the limitations of current predictive methods, we introduce a new deep learning model, GRBMTI, designed to fully utilize both the node and sequence features of miRNA and mRNA to enhance the predictive performance of miRNA-targeting mRNA interactions. For feature extraction, we use RNA2vec to train on RNA data and obtain RNA word vector representations, followed by using CNN and BiGRU to mine RNA sequence features, while GraRep is used for obtaining node features. Finally, DNN is employed to merge sequence and node features, enhancing the integration of features to accurately predict miRNA-mRNA interactions. The GRBMTI model demonstrates robust performance on the MTIS-9214 dataset, with an accuracy of 85.89%, AUC of 0.9389, and AUPR of 0.9392, alongside high cross-dataset consistency. These results underscore its notable referential value for advancing the study of miRNA-target mRNA interactions, indicating its utility and relevance in the field.
Optimizing Staffing for a New Medical Facility
Authors: Irwin R. B. , Le M. A. , Muindi P. M. , Wang D. X. , Lu Y. A. , Koch C. E. , Velumani S.U. 
Published: November 2024

Abstract

When a new medical facility is planned, there is a need for staff members of various job roles and levels. For each of these roles, there are several different classifications for staff. Each of these classification groups have their respective advantages and disadvantages in terms of cost, productivity, new ideas, and other characteristics. These characteristics, which have a continuous range of values, differ for each type of job role. In addition, there are boundary conditions, which only have binary value, that also limit the proportion for each classification group. While the number of classifications is not limited, this publication will consider three primary classifications for staff: early career hires, experienced hires, and (experienced) transfers. This article details a method for using these metrics and boundary conditions to optimize the staffing using a visualization approach. While the equations for the metrics and boundary conditions can be solved directly, they do not answer how the optimum solution is obtained in the way that visualizations can. Since each facility and location may have its own unique requirements, this article discusses general principles and mathematical processes rather than exact prescriptions.
2.xD - Particle-in-Cell simulation of radio-frequency ion thruster RIT-2.5 using cylindrical coordinate system
Authors: Joshi Ninad , Auffarth Philipp , Heiliger Christian 
Published: November 2024

Abstract

A kinetic simulation of radio-frequency plasma is a highly sough tool for ion thrusters.A Particle-in-Cell code, named \emph{PlasmaPIC}, has been developed to simulate plasma inside small-sized thrusters using massive parallization running on High Performance Computing.For particle-based simulation of the plasma in larger structures, the computational power increases rapidly. To overcome this limitation the variant named \emph{PlasmaPIC-Cyl} is developed using polar coordinates that use either symmetry conditions or a partial 3D approach.In this paper, the 2.xD - approach for the simulation of radio-frequency ion thrusters is described.It has been used for detailed simulation of plasma properties like density, the temperature inside the plasma chamber and the extraction of ion beam. The code is employed for the characterization of a radio frequency ion thruster, RIT-2.5, a thruster with a diameter of 2.5 cm. In this paper, the simulations are compared with the measurements so some extend to validate numerical model.We further show its applicability in grid erosion calculations which can be used for lifetime prediction of the gridded thrusters.
The effect of large-channel endoscopy combined with visualization of trephines for foraminoplasty on lumbar biomechanics: a finite element analysis
Authors: Li Duohua , Fu Hao , Zhao Sicong , Gao Xiao , Wu Dongying , Feng Hu , Sun Wei 
Published: November 2024

Abstract

Purpose: This study aimed to evaluate the effect of the degree of facet joint resection under the combined action of large-channel endoscopy and visualized trephines on lumbar biomechanics.
Methods: The original CT data of a healthy male volunteer were selected. An L3-5 lumbar spine model, M0,was established via thethree-dimensional finite element method. Different degrees of resection of the superior articular process of L4 were simulated via a visualized trephine during the operation, and six models were established (M1: tip resection; M2: resection of the ventral 1/3; M3: resection of the ventral 1/2; M4: resection of the ventral 2/3; M5: resection of the ventral 3/4; and M6: complete resection). Loads were applied to the model to simulate six motions of flexion, extension, left/right lateral bending, and left/right rotation. The stress distributions of the vertebral body, intervertebral disc and articular cartilage of the L3-4 segment and adjacent segments wereobserved.
Results: When the degree of facet joint resection does not exceed 1/2, there is no significant change in the pressure of the vertebral body, intervertebral disc and facet joint in M1, M2 and M3. When the degree of facet joint resection exceeds 1/2, the pressure on the L3, L4 and L5 vertebral bodies in models M4, M5 and M6 increases significantly. The pressure of the L3-4 intervertebral disc increases while the pressure of the L4-5 intervertebral disc tends to decrease. The pressure of the facet joints on both sides of L3-4 increases, and the pressure increase on the left facet joint is relatively large.
Conclusion: When more than half of the superior articular process of L4 is resected under large-channel endoscopy, the stress on the vertebral body, intervertebral disc and articular cartilage of the L3-4 segment increases, which may cause iatrogenic instability but has no significant effect on the stress on the vertebral body or intervertebral disc of adjacent segments.
DARUMA: Your gateway to fast and easy prediction of intrinsically disordered regions
Authors: Shimizu Itsuki , Ida Takuya , Ozawa Yuhei , Fukuchi Satoshi , Anbo Hiroto 
Published: November 2024

Abstract

Background Intrinsically disordered proteins (IDPs) are proteins that contain intrinsically disordered regions (IDRs), which lack stable three-dimensional structures under physiological conditions. These regions are known to play crucial roles in many biological processes. While IDRs can be predicted from their amino acid sequences, and several accurate IDR prediction programs have been developed, such programs often require substantial computational resources, including long execution time, large databases for homology searches, and advanced computer architectures. Since DNA sequence data continues to grow rapidly, particularly at a genomic scale, there is an increasing need for fast and accurate IDR prediction programs that demand fewer computational resources.Results In this study, we developed DARUMA (Disorder order clAssifier by Rapid and User-friendly MAchine), an IDR prediction program designed for speed and ease of use. DARUMA achieves fast performance by avoiding iterative homology searches while delivering accuracy comparable to the latest predictors that use sequence profiles. In addition to the advantage of execution time, DARUMA requires no additional homology search programs and operates using standard Python libraries, making it easy to install and run on users’ own environments without the need for specialized computational resources.Conclusions DARUMA is a fast, accurate, and user-friendly IDR prediction tool available as both a web interface and a stand-alone distribution at https://antepontem.org/daruma/. Its unique features make it accessible to a wide range of users and a valuable tool for IDR research.
Towards low-cost and PCR free field-based community metabarcoding
Authors: Plewnia Amadeus , Krehenwinkel Henrik , Heine Christopher 
Published: November 2024

Abstract

1. DNA metabarcoding has revolutionized our ability to monitor ecosystems. However, the method is still rarely used in developing countries where resources are limited and fieldwork is challenging. 2. To overcome this, we designed a comprehensive workflow allowing rapid community metabarcoding with minimum, self-manufacturable equipment in the field. We combine cellulose-based fast DNA extraction, optimized for environmental samples, with isothermal recombinase polymerase amplification (RPA). We demonstrate that stepwise RPA-based reamplification allows the introduction of non-homologous sequence adapters and index sequences, allowing the complete isothermal preparation of libraries for high-throughput sequencing. 3. Compared to laboratory-dependent approaches, the novel workflow halves costs and reduces hands on time more than fourfold. Our workflow makes metabarcoding broadly accessible for practitioners, conservationists and researchers without extensive laboratory equipment. Using environmental DNA from fish mock communities and a riverine ecotone, we demonstrate the sensitivity of the workflow and its interconnectivity with existing approaches. 4. Making metabarcoding available to researchers around the globe, our approach signifies a critical contribution to the immense task of characterizing and protecting earth’s biodiversity.
3D Stereo Adaptive Mesh Augmented Lagrangian Digital Image Correlation
Authors: Tong Zixiang , Frolkin Dan , Shi Hongyang , LaRue Trace , Rausch Manuel , Yang Jin 
Published: November 2024

Abstract

Background: 3D Stereo Digital Image Correlation (3D stereo-DIC) is a powerful experimental tool for measuring full-field, three-dimensional surface deformations, especially for non-planar surfaces or out-of-plane displacements. By capturing and comparing digital images of speckle-patterned samples before and after deformation, stereo-DIC can resolve full-field displacement and strain fields.
Objective: However, current post-processing methods in stereo-DIC are less robust compared to 2D DIC, particularly when dealing with heterogeneous deformation fields in complex geometries or with discontinuities. To address these challenges, we recently developed a novel hybrid local subset and global-DIC post-processing algorithm, called the Augmented Lagrangian Digital Image Correlation (ALDIC) method, which ensures global kinematic compatibility while maintaining computational efficiency, and conveniently applies to adaptive meshes.
Methods: ALDIC has already demonstrated strong robustness and precision in 2D-DIC applications. Building on this progress, here we present 3D Stereo Augmented Lagrangian Digital Image Correlation (3D stereo-ALDIC), which integrates the ALDIC principles into the stereo-DIC framework for 3D deformation analyses. To further enhance performance, we implemented cumulative and incremental tracking modes to resolve both small and large deformations. Additionally, the integration of an adaptive quadtree mesh allows the method to handle complex geometries with ease.
Results: Through various case studies, we demonstrate that stereo-ALDIC outperforms conventional local subset-based stereo-DIC methods in both accuracy and robustness, offering significant advancements for 3D measurement. An open-source MATLAB implementation is freely available.
Blockchain Technology and Sustainable Finance: Exploring the Potential for Climate Change Mitigation and Adaptation
Authors: Kouam Arthur William Fodouop 
Published: November 2024

Abstract

This study investigates the integration of blockchain technology into sustainable finance, explicitly examining its potential to enhance climate change mitigation and adaptation strategies. We employ a comprehensive quantitative analysis using a robust methodology. It encompasses descriptive statistics, exploratory factor analysis, correlation analysis, ordinary least squares (OLS) regression, and moderation analysis. The sample comprises 200 organizations engaged in sustainable finance, providing insights into their blockchain practices and sustainability initiatives. Our findings reveal significant correlations between blockchain integration and improved sustainable finance outcomes, emphasizing the role of various barriers that organizations face in adopting this technology. The study advances original contributions by analyzing how blockchain serves as a compliance tool and a catalyst for enhancing transparency and accountability in sustainable finance. By highlighting the statistical relationships among blockchain adoption, stakeholder engagement, and environmental impact, this research offers vital implications for practitioners seeking to leverage blockchain for effective climate action. Overall, the study informs strategies for organizations navigating the complexities of technology adoption within the sustainability framework, thus addressing a critical gap in the existing literature.