Pressure-coupled infusion gyration (PCIG) is a novel promising technique for economical and effective mass production of nanofibres with desirable geometrical characteristics. The average diameter of spun fibres significantly influences the structural, mechanical and physical properties of the produced fibre mats. Having a comprehensive understanding of the significant effects of PCIG experimental variables on the spun fibres is beneficial. In this work, response surface methodology was used to explore the interaction effects and the optimal PCIG experimental variables for achieving the desired morphological characteristics of fibres. The effect of experimental variables, namely solution concentration, infusion (flow) rate, applied pressure and rotational speed, was studied on the average fibre diameter and standard deviations. A numerical model for the interactional influences of experimental variables was developed and optimized with a nonlinear interior point method that can be used as a framework for selecting the optimal conditions to obtain poly-ethylene oxide fibres with desired morphology (targeted average diameter and narrow standard deviation). The adequacy of the models was verified by a set of validation experiments. The results proved that the predicted optimal conditions were able to achieve the average diameter that matched the pre-set desired value with less than 10% of difference.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6545060 | PMC |
| http://dx.doi.org/10.1098/rspa.2019.0008 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Exploring the role of multimodal [F]F-PSMA-1007 PET/CT and multiparametric MRI data in predicting ISUP grading of primary prostate cancer.
Eur J Nucl Med Mol Imaging
January 2025
The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
Purpose: The study explores the role of multimodal imaging techniques, such as [F]F-PSMA-1007 PET/CT and multiparametric MRI (mpMRI), in predicting the ISUP (International Society of Urological Pathology) grading of prostate cancer. The goal is to enhance diagnostic accuracy and improve clinical decision-making by integrating these advanced imaging modalities with clinical variables. In particular, the study investigates the application of few-shot learning to address the challenge of limited data in prostate cancer imaging, which is often a common issue in medical research.
View Article and Find Full Text PDFDistributed communication interference resource scheduling using the master-slave parallel scheduling genetic algorithm.
Sci Rep
January 2025
Rocket Force University of Engineering, Xi'an, 710025, P. R. China.
With the increasing intelligence and diversification of communication interference in recent years, communication interference resource scheduling has received more attention. However, the existing interference scenario models have been developed mostly for remote high-power interference with a fixed number of jamming devices without considering power constraints. In addition, there have been fewer scenario models for short-range distributed communication interference with a variable number of jamming devices and power constraints.
View Article and Find Full Text PDFUnsupervised data imputation with multiple importance sampling variational autoencoders.
Sci Rep
January 2025
School of Mathematics and Statistics, Shaoguan University, Shaoguan, 512005, China.
Recently, deep latent variable models have made significant progress in dealing with missing data problems, benefiting from their ability to capture intricate and non-linear relationships within the data. In this work, we further investigate the potential of Variational Autoencoders (VAEs) in addressing the uncertainty associated with missing data via a multiple importance sampling strategy. We propose a Missing data Multiple Importance Sampling Variational Auto-Encoder (MMISVAE) method to effectively model incomplete data.
View Article and Find Full Text PDFDifferences in treatment outcome between translational platforms in developing therapies for gastrointestinal cancers.
Eur J Pharmacol
January 2025
Department of Pharmacodynamics, Medical University of Bialystok, Mickiewicza 2C, 15-222 Białystok, Poland. Electronic address:
The variability in translational models profoundly impacts the outcomes and predictive value of preclinical studies for gastrointestinal (GI) cancer treatments. Preclinical models, including 2D cell cultures, 3D organoids, patient-derived xenografts (PDXs), and animal models, provide distinct advantages and limitations in replicating the complex tumor microenvironment (TME) of human cancers. Each model's unique biological and structural differences contribute to discrepancies in treatment responses, challenging the direct translation of experimental results to clinical settings.
View Article and Find Full Text PDFInvestigating cognitive-physical task interaction during self-paced cycling: a Granger Causality study.
Psychol Sport Exerc
January 2025
Mind Brain and Behavior Research Center, CIMCYC-UGR, University of Granada, Spain; Department of Experimental Psychology, University of Granada, Spain.
Self-pacing physical exercise is thought to rely on high-order cognitive processing (e.g., attentional control to monitor afferent cardiovascular feedback for exercise goals).
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!