The increasing demand for sustainable and eco-friendly materials has spurred significant interest in natural fibers as alternatives to synthetic reinforcements in composite applications. This study aims to explore the potential of Lablab purpureus fibers (LPFs) as sustainable materials by employing advanced characterization techniques and machine learning-driven analysis. Chemical analysis identified LPFs' primary composition as cellulose (72.34 %), hemicellulose (11.46 %), and lignin (8.99 %), with minor components including wax (3.45 %) and ash (2.59 %). The average fiber diameter was measured at 237.95 μm, with a density of 1.24 g/cm, making LPFs lightweight yet robust. Mechanical testing across varying relative humidity (RH) levels revealed a decrease in tensile properties, with fracture stress declining from 420 MPa at 24 % RH to 350 MPa at 81 % RH. X-ray diffraction (XRD) analysis demonstrated a crystallinity index (CI) of 74.62 % and a crystalline size of 8.73 nm, indicating high structural integrity. Fourier Transform Infrared (FTIR) spectroscopy, combined with Principal Component Analysis (PCA), provided insights into the chemical bonds within the fibers, confirming the presence of cellulose I and cellulose II polymorphs. Thermogravimetric Analysis (TGA) highlighted thermal degradation stages, with hemicellulose decomposition at 220-315 °C, cellulose decomposition at 315-400 °C, and lignin degradation above 400 °C, showcasing thermal stability up to 320 °C. Hydrothermal absorption behavior, analyzed through K-means clustering, revealed distinct absorption patterns, with a maximum moisture uptake of 12.3 % at 81 % RH. Biodegradability tests indicated increased decomposition with higher RH, peaking at 81 % RH with a weight loss of 68.57 % over 16 days. Scanning Electron Microscopy (SEM) revealed intricate fiber morphology, including layered transitions, internal voids, and a honeycomb-like surface structure. Compared to other natural fibers such as Cissus quadrangularis (CI: 82.73 %) and lavender (CI: 65 %), LPFs exhibit a balanced combination of mechanical strength, thermal stability, and biodegradability, making them promising candidates for biocomposites and eco-friendly materials. These findings, supported by machine learning-driven insights, position LPFs as a sustainable alternative to synthetic fibers in industrial applications.
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http://dx.doi.org/10.1016/j.ijbiomac.2025.141589 | DOI Listing |
Front Artif Intell
February 2025
Department of Computer Science & Engineering, Indian Institute of Technology Ropar, Rupnagar, India.
Machine learning techniques have emerged as a promising tool for efficient cache management, helping optimize cache performance and fortify against security threats. The range of machine learning is vast, from reinforcement learning-based cache replacement policies to Long Short-Term Memory (LSTM) models predicting content characteristics for caching decisions. Diverse techniques such as imitation learning, reinforcement learning, and neural networks are extensively useful in cache-based attack detection, dynamic cache management, and content caching in edge networks.
View Article and Find Full Text PDFBiofactors
March 2025
Department of Colorectal Surgery, Changhai Hospital, Naval Medical University, Shanghai, China.
Colorectal cancer (CRC) exhibits a complex tumor microenvironment with significant cellular heterogeneity, particularly involving cancer-associated fibroblasts that influence tumor behavior and metastasis. This study integrated single-cell RNA sequencing and spatial transcriptomics to dissect fibroblast heterogeneity in CRC. Data processing employed Seurat for quality control, principal component analysis for dimensionality reduction, and t-Distributed Stochastic Neighbor Embedding for visualization.
View Article and Find Full Text PDFSci Rep
March 2025
Jiangxi Provincial Key Laboratory of Respiratory Diseases, Jiangxi Institute of Respiratory Diseases, The Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, China.
Sepsis represents a significant global health challenge, necessitating early detection and effective treatment for improved outcomes. While traditional inflammatory markers facilitate the diagnosis of sepsis, the aspect of immune suppression remains poorly addressed. This study aimed to identify critical immune-related genes (IIRGs) associated with sepsis through genomic analysis and machine learning techniques, thereby enhancing diagnostic and treatment response predictions.
View Article and Find Full Text PDFNat Commun
March 2025
Department of Chemistry, University of Oxford, Oxford, UK.
The structure of amorphous silicon has been studied for decades. The two main theories are based on a continuous random network and on a 'paracrystalline' model, respectively-the latter defined as showing localized structural order resembling the crystalline state whilst retaining an overall amorphous network. However, the extent of this local order has been unclear, and experimental data have led to conflicting interpretations.
View Article and Find Full Text PDFOTA Int
June 2025
Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA.
Introduction: Prediction of nonhome discharge after open reduction internal fixation (ORIF) of distal femur fractures may facilitate earlier discharge planning, potentially decreasing costs and improving outcomes. We aim to develop algorithms predicting nonhome discharge and time to discharge after distal femur ORIF and identify features important for model performance.
Methods: This is a retrospective cohort study of adults in the American College of Surgeons National Surgical Quality Improvement Program database who underwent distal femur ORIF between 2010 and 2019.
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