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Durability of tannin-citric acid modified Scots pine against weathering and fungal exposures
No full textWood modification is essential to enhance the performance and longevity of wood as a material while maintaining its natural and sustainable characteristics. By modifying wood, its properties can be significantly improved without the need for toxic preservatives. Thus bio-based wood modification is vital for enhancing wood properties sustainably, reducing environmental impact, and meeting the demand for eco-friendly, non-toxic, and renewable material solutions. In this study, bio-based formulae were used on pine sapwood for enhanced decay and weathering resistance. Quebracho tannin solutions (20 %) with different concentrations of citric acid (CA) as a cross-linker were prepared and then vacuum-pressure impregnated in Scots pine sapwood before curing them at 140  C. The durability performance of untreated and tannin-impregnated wood samples was assessed against accelerated weathering, mould growth and brown rot decay fungus Coniophora puteana (Schumach.) P. Karst. Experimental results showed that the weathering resistance was apparently improved by tannin modification with higher levels of CA, as reflected by color stability, crack formation and contact angle. While tannin modification increased mould susceptibility, the decay resistance was notably enhanced in all modified wood samples. The acidity of the formulas at high CA levels may increase the risks of hydrolytic degradation of wood, and thus, low CA concentrations (e.g. 2 %) appear optimal for balancing performance and chemical stability. These findings underscore the potential of 100 % bio-based tannin-CA systems to enhance wood durability, offering a promising pathway for sustainable wood protection strategies
Stepper Motor Load Estimation Using a Neural Network
No full textBipolar stepper motors are common in scenarios that require low-cost precision including 3d printers. The ability to estimate stepper motor load provides an opportunity for health monitoring. For example, excessive load estimates may indicate a future problem in the driven system such as bearing wear. Stepper motor drivers often have built-in stall detection strategies that exploit the driver\u27s precise knowledge of the high-frequency voltage pulses being sent to the motor. The goal of this work was to develop a retrofit approach to stepper motor load estimation using externally measured voltage, current, and speed. A stepper motor dynamometer was created to generate motor responses for repeatable applied loads using a d.c. motor. A classification neural network was successfully trained to estimate four different loads at two speeds using images of the sensed current versus voltage Lissajous plot. The measurement system was implemented using dSPACE, however, its signal processing and sensing suite was not exotic and could be implemented on an embedded processor of opportunity. The neural network was implemented on an analysis computer that received batch samples of current, voltage and speed at 1 Hz. The main contribution of this work was to illustrate the feasibility of a retrofit solution for load estimation. Extension to continuous load estimation is left for future work
Overcoming Variable Illumination in Photovoltaic Snow Monitoring: A Real-Time Robust Drone-Based Deep Learning Approach
Get PDFSnow accumulation on photovoltaic (PV) panels can cause significant energy losses in cold climates. While drone-based monitoring offers a scalable solution, real-world challenges like varying illumination can hinder accurate snow detection. We previously developed a YOLO-based drone system for snow coverage detection using a Fixed Thresholding segmentation method to discriminate snow from the solar panel; however, it struggled in challenging lighting conditions. This work addresses those limitations by presenting a reliable drone-based system to accurately estimate the Snow Coverage Percentage (SCP) over PV panels. The system combines a lightweight YOLOv11n-seg deep learning model for panel detection with an adaptive image processing algorithm for snow segmentation. We benchmarked several segmentation models, including MASK R-CNN and the state-of-the-art SAM2 segmentation model. YOLOv11n-seg was selected for its optimal balance of speed and accuracy, achieving 0.99 precision and 0.80 recall. To overcome the unreliability of static thresholding under changing lighting, various dynamic methods were evaluated. Otsu’s algorithm proved most effective, reducing the absolute error of the mean in SCP estimation to just 1.1%, a significant improvement over the 13.78% error from the previous fixed-thresholding approach. The integrated system was successfully validated for real-time performance on live drone video streams, demonstrating a highly accurate and scalable solution for autonomous snow monitoring on PV systems
Designing AAC for use in Social and Community Contexts: a scoping review
No full textIt is a right for people who use AAC to communicate with other people and engage with their communities. This scoping review explores how AAC is currently being used by people with communication disabilities in social and community contexts and the impact the design of AAC systems can have on this communication. A total of 13 studies were included that proposed new AAC system designs, conducted interviews with people who use AAC and their chosen communication partners, or performed an AAC intervention. Six themes emerged from these studies that indicate people who use AAC desire interaction with others, can benefit from greater agency in the communication process, can leverage the script-like nature of certain interactions to improve their communication competency, make use of online and asynchronous methods of communication, use multiple modes of communication and AAC content representation, and can benefit from instruction in social communication and community engagement. Suggestions for future research on how to incorporate each theme into the design of new AAC systems are also provided
Effect of Microbially Induced Carbonate Precipitation (MICP) via Denitrification on the Dissolution Rate and Stability of Gypsum Mine Pillars: Laboratory Scale Experiments
No full textGypsum mine pillars in flooded abandoned mines are susceptible to water dissolution, particularly when flowing groundwater is encountered. Over time, this dissolution of gypsum weakens the pillars and increases the risk of collapse. Traditional stabilization methods, such as grouting and structural reinforcement, have limitations in terms of cost, feasibility, and environmental impact. This study investigates the potential of a nature-based biocementation method, i.e., Microbially Induced Carbonate Precipitation (MICP) via denitrification, as an alternative approach to enhance the stability of gypsum mine pillars. Laboratory experiments were conducted to evaluate the effects of MICP treatment on gypsum cores subjected to varying water flow rates. Samples were treated with different treatment cycles and two main treatment procedures (submerging and spraying), and their resistance to dissolution was evaluated. The results of the submerging method showed promising results, indicating that MICP treatment can decrease the dissolution rate of the gypsum pillar. The results also showed that increasing the number of MICP treatment cycles significantly reduces gypsum dissolution and the unconfined compression strength of dissolved samples. Scanning electron microscopy and energy dispersive X-ray spectroscopy confirmed that the calcium carbonate precipitated as calcite, forming a protective crust that mitigates dissolution. The results of the spraying method were not satisfactory, possibly due to fast evaporation of water from the surface of treated samples
NExNet Seg: Neuron Expansion Network for Medical Image Segmentation
No full textThe advent of deep learning (DL) has significantly advanced artificial intelligence, driving notable progress in fields such as language translation, object recognition, and recommendation systems. Despite these successes, the computational complexity of advanced DL models continues to impede their practical deployment, particularly in clinical settings. Addressing this challenge, we introduce NExNet Seg, the Neuron Expansion Network for Medical Image Segmentation. Inspired by Progressively Expanded Neuron (PEN) structures and Manhattan Self-Attention (MaSA) mechanisms, NExNet Seg achieves exceptional accuracy with high parameter efficiency. It substantially reduces computational overhead, making it especially suitable for segmentation tasks in skin lesions and colorectal cancer using dermoscopic and endoscopic imagery. Empirical evaluations conducted on publicly available datasets, including PH2, ISIC (2016-2018), CVC Clinic, and Kvasir, demonstrate that NExNet Seg consistently outperforms current state-of-the-art methods in terms of accuracy, computational efficiency, and generalization capability. These results highlight its potential as an effective, scalable solution for clinical deployment in medical image segmentation
Investigation of Modified Asphalt Binders through Fourier Transformation Infrared Spectroscopic Analysis
No full textModification of asphalt binders causes changes in asphalt chemistry that can impact performance. The main goal of this study was to look for any appreciable alterations in asphalt chemistry brought on by aging and modification. The secondary objective was to explore any indication of changes in mechanical properties from spectroscopic analysis. For this study, two neat performance-grade (PG) binders (PG 64-22) from two different sources were modified with different combinations of three types of commonly practiced modifiers: polyphosphoric acid, styrene-butadiene-styrene, and Cloisite 11B nanoclay. Many researchers consider nanoclay as a potential alternative to conventional modifiers. Thus, Cloisite 11B nanoclay has also been considered in this study. To this end, Fourier transform infrared (FTIR) spectroscopic analysis was employed on these sample binders, followed by further chemical analysis, including pH measurements and saturate, aromatic, resin, and asphaltene (SARA) analysis. Furthermore, mechanical test results were compared to find any possible indications of changes in the rheological properties from spectroscopic analysis. It was observed that modification caused significant variations in asphalt chemistry. The sulfoxide index (S=O) was found to be related to the pH values, particularly for the polymer-modified binder samples. Moreover, the carbonyl index (C=O) increased significantly with aging, which was eventually found to be an identification parameter for aging. In addition, changes in aromatic and aliphatic indices were able to explain the variations observed in chemical fractions in different aging conditions after modification. Finally, this study was able to find some trends between rheological properties and changes in the ratio of bonding
Cryogenic hybrid magnonic circuits based on spalled YIG thin films
Get PDFYttrium iron garnet (YIG) magnonics has garnered significant research interest because of the unique properties of magnons (quasiparticles of collective spin excitation) for signal processing. In particular, hybrid systems based on YIG magnonics show great promise for quantum information science due to their broad frequency tunability and strong compatibility with other platforms. However, their broad applications have been severely constrained by substantial microwave loss in the gadolinium gallium garnet (GGG) substrate at cryogenic temperatures. In this study, we demonstrate that YIG thin films can be spalled from YIG/GGG samples. Our approach is validated by measuring hybrid devices comprising superconducting resonators and spalled YIG films, which exhibit anti-crossing features that indicate strong coupling between magnons and microwave photons. Such new capability of separating YIG thin films from GGG substrates via spalling and the integrated superconductor-YIG devices represent a significant advancement for integrated magnonic devices, paving the way for advanced magnon-based coherent information processing
First-Principles Study of the Heterostructure, ZnSb Bilayer/h-BN Monolayer for Thermoelectric Applications
Get PDFZnSb is widely recognized as a promising thermoelectric material in its bulk form, and a ZnSb bilayer was recently synthesized from the bulk. In this study, we designed a vertical van der Waals heterostructure consisting of a ZnSb bilayer and an h-BN monolayer to investigate its electronic, elastic, transport, and thermoelectric properties. Based on density functional theory, the results show that the formation of this heterostructure significantly enhances electron mobility and reduces the bandgap compared to the ZnSb bilayer, thereby increasing its power factor. These findings highlight the potential of the h-BN monolayer–supported ZnSb bilayer heterostructure in thermoelectric applications, where maximizing energy conversion efficiency is essential