Factores clave en la práctica de la Ciencia Abierta. Un análisis multivariado en el contexto universitario = Key Factors in Open Science Practice: A Multivariate Analysis within th... by Sebastián Araya-Pizarro, Héctor García-Leal

“…Data were analyzed using descriptive statistical techniques, association tests, and multivariate analysis (binary logistic regression and K-means algorithm). Results revealed that the willingness to engage in open science is strongly influenced by knowledge, interest, and educational level, and moderately by the participant’s role. …”
Get full text

Static stress triggering of Morocco M 6.9 earthquake on 9 September 2023 by WANG Runyan, WAN Yongkui, GUAN Zhaoxuan, HUANG Shaohua, GU Peiyuan, SONG Zeyao, ZHOU Mingyue

“…Methods  Using the "central solution" algorithm, two possible nodal planes of the earthquake were determined, and the local stress field was projected onto these two planes. …”
Get full text

Bundled assessment to replace on-road test on driving function in stroke patients: a binary classification model via random forest by Lu Huang, Lu Huang, Xin Liu, Jiang Yi, Yu-Wei Jiao, Tian-Qi Zhang, Guang-Yao Zhu, Shu-Yue Yu, Zhong-Liang Liu, Min Gao, Xiao-Qin Duan

“…Furthermore, the established random forest classification model has demonstrated efficacy in predicting on-road test outcomes, which is worthy of further clinical application.…”
Get full text

Exploring the role of neutrophil extracellular traps in neuroblastoma: identification of molecular subtypes and prognostic implications by Can Qi, Can Qi, Ziwei Zhao, Lin Chen, Le Wang, Yun Zhou, Guochen Duan, Guochen Duan

“…Univariate Cox analysis and the LASSO algorithm were used to identify biomarkers for prognosis. …”
Get full text

Mapping recent timber harvest activity in a temperate forest using single date airborne LiDAR surveys and machine learning: lessons for conservation planning by G. Burch Fisher, Andrew J. Elmore, Matthew C. Fitzpatrick, Darin J. McNeil, Jeff W. Atkins, Jeffery L. Larkin

“…In this paper, we develop a timber harvest mapping workflow using machine learning (XGBoost algorithm) and single campaign airborne light detection and ranging (LiDAR) surveys for the state of Pennsylvania, USA. …”
Get full text

Permeability evolution of the thick Cretaceous aquifer and the high-level bed separation water accumulation model during coal mining by Wei QIAO, Xiangsheng MENG, Juan YANG, Liangang LI, Qijing LIANG, Mengnan LIU, Zhihe TAO, Changmin HAN, Weiteng KONG

“…Hydraulic tomography inversion technology, based on the Simultaneous Sequential Linear Estimation (SimSLE) algorithm, was used to analyze the permeability evolution of the aquifer during mining. …”
Get full text

Comprehensive analyses of telomerase component DKC1 and its association with clinical, molecular and immune landscapes in uterine corpus endometrial carcinoma by Chenxi Sun, Xu Liu, Tiantian Liu, Chenliu Fan, Yang Jiang, Binggen Li, Huiyang Yuan, Chengyun Zheng, Dawei Xu

“…Higher DKC1 expression predicted shorter patient overall and progression-free survival. …”
Get full text

Research on entity recognition and alignment of APT attack based on Bert and BiLSTM-CRF by Xiuzhang YANG, Guojun PENG, Zichuan LI, Yangqi LYU, Side LIU, Chenguang LI

“…The attention mechanism is built to highlight key features and convert the vector sequence into an annotation probability matrix. Thirdly, the CRF algorithm is utilized to decode the relationship between the output predicted labels and generate the optimal label sequence. …”
Get full text

Research on entity recognition and alignment of APT attack based on Bert and BiLSTM-CRF by Xiuzhang YANG, Guojun PENG, Zichuan LI, Yangqi LYU, Side LIU, Chenguang LI

“…The attention mechanism is built to highlight key features and convert the vector sequence into an annotation probability matrix. Thirdly, the CRF algorithm is utilized to decode the relationship between the output predicted labels and generate the optimal label sequence. …”
Get full text

The azimuthal correlation between the leading jet and the scattered lepton in deep inelastic scattering at HERA by ZEUS Collaboration, I. Abt, R. Aggarwal, V. Aushev, O. Behnke, A. Bertolin, I. Bloch, I. Brock, N. H. Brook, R. Brugnera, A. Bruni, P. J. Bussey, A. Caldwell, C. D. Catterall, J. Chwastowski, J. Ciborowski, R. Ciesielski, A. M. Cooper-Sarkar, M. Corradi, R. K. Dementiev, S. Dusini, J. Ferrando, B. Foster, E. Gallo, D. Gangadharan, A. Garfagnini, A. Geiser, G. Grzelak, C. Gwenlan, D. Hochman, N. Z. Jomhari, I. Kadenko, U. Karshon, P. Kaur, R. Klanner, U. Klein, I. A. Korzhavina, N. Kovalchuk, M. Kuze, B. B. Levchenko, A. Levy, B. Löhr, E. Lohrmann, A. Longhin, F. Lorkowski, E. Lunghi, I. Makarenko, J. Malka, S. Masciocchi, K. Nagano, J. D. Nam, Yu. Onishchuk, E. Paul, I. Pidhurskyi, A. Polini, M. Przybycień, A. Quintero, M. Ruspa, U. Schneekloth, T. Schörner-Sadenius, I. Selyuzhenkov, M. Shchedrolosiev, L. M. Shcheglova, N. Sherrill, I. O. Skillicorn, W. Słomiński, A. Solano, L. Stanco, N. Stefaniuk, B. Surrow, K. Tokushuku, O. Turkot, T. Tymieniecka, A. Verbytskyi, W. A. T. Wan Abdullah, K. Wichmann, M. Wing, S. Yamada, Y. Yamazaki, A. F. Żarnecki, O. Zenaiev

“…Jets were reconstructed using the $$k_\textrm{T}$$ k T algorithm with the radius parameter $$R = 1$$ R = 1 . …”
Get full text

Identification of veterinary and medically important blood parasites using contrastive loss-based self-supervised learning by Supasuta Busayakanon, Morakot Kaewthamasorn, Natchapon Pinetsuksai, Teerawat Tongloy, Santhad Chuwongin, Siridech Boonsang, Veerayuth Kittichai

“…The input data were subjected to SSL model training using the Bootstrap Your Own Latent (BYOL) algorithm with Residual Network 50 (ResNet50), ResNet101, and ResNet152 as the backbones. …”
Get full text

Groundwater–CO₂ emissions relationship in Dutch peatlands derived by machine learning using airborne and ground-based eddy covariance data by L. M. van der Poel, L. V. Bataille, B. Kruijt, W. Franssen, W. Jans, J. Biermann, A. Rietman, A. J. V. Buzacott, Y. van der Velde, R. Boelens, R. W. A. Hutjes

“…Using spatiotemporal data, we train and optimize a boosted regression tree (BRT) machine learning algorithm to predict immediate CO<span class="inline-formula">₂</span> fluxes and use Shapley values and various simulations to interpret the model's outputs. …”
Get full text

Diffuse attenuation coefficient and bathymetry retrieval in shallow water environments by integrating satellite laser altimetry with optical remote sensing by Changda Liu, Huan Xie, Qi Xu, Jie Li, Yuan Sun, Min Ji, Xiaohua Tong

“…Finally, the neural network model accurately predicted the bathymetry in the two regions. The accuracy of the bathymetric maps improved significantly with seafloor classification, as indicated by reductions in root mean square error (RMSE) of 0.12 m and 0.15 m, and in mean absolute percentage error (MAPE) by 2.24 % and 5.87 %, respectively. …”
Get full text

Age and sex specific thresholds for risk stratification of cardiovascular disease and clinical decision making: prospective open cohort study by Matthew Sperrin, Angela M Wood, Lois Kim, Zhe Xu, Emanuele Di Angelantonio, Stephen Kaptoge, Juliet Usher-Smith, Matthew Arnold, Lisa Pennells, Ryan Chung

“…Objective To quantify the potential advantages of using 10 year risk prediction models for cardiovascular disease, in combination with risk thresholds specific to both age and sex, to identify individuals at high risk of cardiovascular disease for allocation of statin treatment.Design Prospective open cohort study.Setting Primary care data from the UK Clinical Practice Research Datalink GOLD, linked with hospital admissions from Hospital Episode Statistics and national mortality records from the Office for National Statistics in England, 1 January 2006 to 31 May 2019.Participants 1 046 736 individuals (aged 40-85 years) with no cardiovascular disease, diabetes, or a history of statin treatment at baseline using data from electronic health records.Main outcome measures 10 year risk of cardiovascular disease, calculated with version 2 of the QRISK cardiovascular disease risk algorithm (QRISK2), with two main strategies to identify individuals at high risk: in strategy A, estimated risk was a fixed cut-off value of ≥10% (ie, as per the UK National Institute for Health and Care Excellence guidelines); in strategy B, estimated risk was ≥10% or ≥90th centile of age and sex specific risk distributions.Results Compared with strategy A, strategy B stratified 20 241 (149.8%) more women aged ≤53 years and 9832 (150.2%) more men aged ≤47 years as having a high risk of cardiovascular disease; for all other ages the strategies were the same. …”
Get full text

4D-CTA image and geometry dataset for kinematic analysis of abdominal aortic aneurysmsZenodo by Mostafa Jamshidian, Adam Wittek, Saeideh Sekhavat, Farah Alkhatib, Jens Carsten Ritter, Paul M. Parizel, Donatien Le Liepvre, Florian Bernard, Ludovic Minvielle, Antoine Fondanèche, Jane Polce, Christopher Wood, Karol Miller

“…The images were acquired at Fiona Stanley Hospital in Western Australia and provided to the researchers at the Intelligent Systems for Medicine Laboratory at The University of Western Australia (ISML-UWA), where image-based AAA kinematic analysis was performed using a newly created algorithm, as described in [1]. The AAA geometries were extracted using an automated image processing pipeline comprising AI-based segmentation with PRAEVAorta software by NUREA (https://www.nurea-soft.com/), automated post-processing with the ISML-UWA in-house code (https://arxiv.org/abs/2403.07238), and surface model extraction using the freely available BioPARR (Biomechanics-based Prediction of Aneurysm Rupture Risk) (https://bioparr.mech.uwa.edu.au/) and 3D Slicer (https://www.slicer.org/) software packages [2,3]. …”
Get full text

Conv1D-LSTM: Autonomous Breast Cancer Detection Using a One-Dimensional Convolutional Neural Network With Long Short-Term Memory by Mitanshi Rastogi, Meenu Vijarania, Neha Goel, Akshat Agrawal, Cresantus N. Biamba, Celestine Iwendi

Get full text

Spatial and Temporal Variability of Rainfall Erosivity in the Niyang River Basin by Qingqin Bai, Lei Wang, Yangzong Cidan

“…This study enhances the understanding of rainfall erosive forces in the plateau region and provides a scientific basis for predicting soil loss, developing effective erosion control measures, and ensuring sustainable land use.…”
Get full text

Clinical and Epidemiological Manifestations of Ixodic Tick-Borne Borreliosis Foci in the Tomsk region by O. V. Voronkova, E. N. Ilyinskikh, A. A. Rudikov, T. N. Poltoratskaya, I. E. Esimova, L. V. Lukashova, M. R. Karpova

“…The study of the genotypic diversity of pathogenic borrelias in relation to the species diversity of vectors, the analysis of the clinical manifestations of different etiological variants of tick-borne borreliosis (mono- and mixed infections), as well as the development of an algorithm for differential diagnostic search and a model for predicting the outcomes of the infectious process in tick-borne borreliosis and mixed infections are priority directions of problem-oriented scientific research in Tomsk region.…”
Get full text

Evaluating the Pattern of Forest Cover Changes Using Fuzzy Object-Oriented Techniques (Case Study: Kaleybar County) by Majid Pishnamaz Ahmadi, keyvan Mohammadzadeh, seyed Asadolah Hejazi

“…The results were then calculated and finalized in ArcGIS software after accurate evaluation. 3-Results and Discussion In this research, images in 200 scales sorted consecutively from 1 to 200 were segmented using low to high multi-functional hierarchical segmentation approach with shape coefficient of 0.4 and compression coefficient of 0.5 in order to construct LV graphs and the appropriate scales for image segmentation were determined using the plotted graphs. By predicting the appropriate scale for creating image units using the algorithm (ESP), the scale of 15 with coefficients of shape and compression 0.3 and 0.5 respectively was scaled as the appropriate scale for extraction of Landsat 5 and 7 satellite images, and the scale of 130 with shape coefficient of 0.4 and compression coefficient of 0.5 was chosen as the appropriate scale for Landsat 8 satellite OLI images. …”
Get full text

miR-146a deficiency in hematopoietic cells is not involved in the development of atherosclerosis. by Alberto Del Monte, Ana B Arroyo, María J Andrés-Manzano, Nuria García-Barberá, María S Caleprico, Vicente Vicente, Vanessa Roldán, Rocío González-Conejero, Constantino Martínez, Vicente Andrés

Get full text