Adhesive Through-Reinforcement Improves the Fracture Toughness of a Laminated Birch Wood Composite

In this paper we test the hypothesis that adhesive through-reinforcement in combination with glass-fibre reinforcement of adhesive bond lines will significantly improve the fracture toughness of a laminated birch wood composite. We test this hypothesis using a model composite consisting of perforated veneer that allowed a polyurethane adhesive to penetrate and reinforce veneers within the composite. Model composite specimens were tested for mode I fracture properties, and scanning electron microscopy was used to examine the microstructure of fracture surfaces. Our results clearly show that through-reinforcement, and also reinforcing adhesive bond lines with glass-fibre, significantly improved fracture toughness of the birch wood composite. Our results also indicate that improvements in fracture toughness depended on the level of reinforcement. Improvements in fracture toughness were related to the ability of the reinforcement to arrest crack development during fracture testing and the fibre bridging effect of glass-fibre in adhesive bond lines. We conclude that through-reinforcement is an effective way of improving the fracture toughness of laminated wood composites, but further research is needed to develop practical ways of creating such reinforcement in composites that more closely resemble commercial products

Few-shot Class-incremental Audio Classification Using Adaptively-refined Prototypes

New classes of sounds constantly emerge with a few samples, making it challenging for models to adapt to dynamic acoustic environments. This challenge motivates us to address the new problem of few-shot class-incremental audio classification. This study aims to enable a model to continuously recognize new classes of sounds with a few training samples of new classes while remembering the learned ones. To this end, we propose a method to generate discriminative prototypes and use them to expand the model's classifier for recognizing sounds of new and learned classes. The model is first trained with a random episodic training strategy, and then its backbone is used to generate the prototypes. A dynamic relation projection module refines the prototypes to enhance their discriminability. Results on two datasets (derived from the corpora of Nsynth and FSD-MIX-CLIPS) show that the proposed method exceeds three state-of-the-art methods in average accuracy and performance dropping rate.Comment: 5 pages,2 figures, Accepted by Interspeech 202

Few-shot Class-incremental Audio Classification Using Stochastic Classifier

It is generally assumed that number of classes is fixed in current audio classification methods, and the model can recognize pregiven classes only. When new classes emerge, the model needs to be retrained with adequate samples of all classes. If new classes continually emerge, these methods will not work well and even infeasible. In this study, we propose a method for fewshot class-incremental audio classification, which continually recognizes new classes and remember old ones. The proposed model consists of an embedding extractor and a stochastic classifier. The former is trained in base session and frozen in incremental sessions, while the latter is incrementally expanded in all sessions. Two datasets (NS-100 and LS-100) are built by choosing samples from audio corpora of NSynth and LibriSpeech, respectively. Results show that our method exceeds four baseline ones in average accuracy and performance dropping rate. Code is at https://github.com/vinceasvp/meta-sc.Comment: 5 pages, 3 figures, 4 tables. Accepted for publication in INTERSPEECH 202

Low-Complexity Acoustic Scene Classification Using Data Augmentation and Lightweight ResNet

We present a work on low-complexity acoustic scene classification (ASC) with multiple devices, namely the subtask A of Task 1 of the DCASE2021 challenge. This subtask focuses on classifying audio samples of multiple devices with a low-complexity model, where two main difficulties need to be overcome. First, the audio samples are recorded by different devices, and there is mismatch of recording devices in audio samples. We reduce the negative impact of the mismatch of recording devices by using some effective strategies, including data augmentation (e.g., mix-up, spectrum correction, pitch shift), usages of multi-patch network structure and channel attention. Second, the model size should be smaller than a threshold (e.g., 128 KB required by the DCASE2021 challenge). To meet this condition, we adopt a ResNet with both depthwise separable convolution and channel attention as the backbone network, and perform model compression. In summary, we propose a low-complexity ASC method using data augmentation and a lightweight ResNet. Evaluated on the official development and evaluation datasets, our method obtains classification accuracy scores of 71.6% and 66.7%, respectively; and obtains Log-loss scores of 1.038 and 1.136, respectively. Our final model size is 110.3 KB which is smaller than the maximum of 128 KB.Comment: 5 pages, 5 figures, 4 tables. Accepted for publication in the 16th IEEE International Conference on Signal Processing (IEEE ICSP

Effects of adhesive z-connections on the properties of a model wood composite

Adhesive is a costly and critical component of wood composites. The relationship between adhesive distribution and properties of wood composites has been explored, but few studies have attempted to alter the distribution of adhesive in wood composites as a way of improving their properties. In this thesis, I hypothesize that creating a 3-dimensionally inter-connected adhesive network by introducing adhesive Z-connections will improve two key properties of wood composites (thickness swelling and fracture toughness). Both experiments and computer simulation (finite element analysis) were carried out to test this hypothesis. I developed a methodology to precisely perforate veneer to facilitate the creation of adhesive Z-connections when the composite was pressed. Adhesive Z-connections are defined as the cured adhesive distributed in the Z- (thickness) direction (in addition to the X-Y directions) of the laminated wood composite due to the perforation in veneer. I examined factors affecting the ability of Z-connections to improve dimensional stability and fracture toughness of a model wood composite. I visualized the adhesive distribution in the composite in 2D and 3D using macro-photography, X-ray micro-computed tomography and scanning electron microscopy. Significant improvements in dimensional stability and fracture toughness of some of the composites were observed. Key parameters affecting the ability of adhesive Z-connections to reduce thickness swelling were diameter and spatial arrangement of Z-connections, adhesive level and wood species used to make the composite. Key parameters affecting the ability of adhesive to increase the fracture toughness of a model wood composite were area-density of Z-connections and reinforcement of the adhesive in the composite. I conclude that introducing adhesive Z-connections can reduce thickness swelling and enhance fracture toughness of wood composites, but the effectiveness of such an approach is affected by wood species, area-density and spatial arrangement of the Z-connections. I discuss the implications of my findings for the development of wood composites with enhanced dimensional stability and fracture toughness and further research needed to capitalize on the concept of creating an inter-connected 3D adhesive network in wood composites by introducing adhesive Z-connections.Forestry, Faculty ofGraduat

Adhesive Through-Reinforcement Improves the Fracture Toughness of a Laminated Birch Wood Composite

In this paper we test the hypothesis that adhesive through-reinforcement in combination with glass-fibre reinforcement of adhesive bond lines will significantly improve the fracture toughness of a laminated birch wood composite. We test this hypothesis using a model composite consisting of perforated veneer that allowed a polyurethane adhesive to penetrate and reinforce veneers within the composite. Model composite specimens were tested for mode I fracture properties, and scanning electron microscopy was used to examine the microstructure of fracture surfaces. Our results clearly show that through-reinforcement, and also reinforcing adhesive bond lines with glass-fibre, significantly improved fracture toughness of the birch wood composite. Our results also indicate that improvements in fracture toughness depended on the level of reinforcement. Improvements in fracture toughness were related to the ability of the reinforcement to arrest crack development during fracture testing and the fibre bridging effect of glass-fibre in adhesive bond lines. We conclude that through-reinforcement is an effective way of improving the fracture toughness of laminated wood composites, but further research is needed to develop practical ways of creating such reinforcement in composites that more closely resemble commercial products

Mass Sensitivity Optimization of a Surface Acoustic Wave Sensor Incorporating a Resonator Configuration

The effect of the sensitive area of the two-port resonator configuration on the mass sensitivity of a Rayleigh surface acoustic wave (R-SAW) sensor was investigated theoretically, and verified in experiments. A theoretical model utilizing a 3-dimensional finite element method (FEM) approach was established to extract the coupling-of-modes (COM) parameters in the absence and presence of mass loading covering the electrode structures. The COM model was used to simulate the frequency response of an R-SAW resonator by a P-matrix cascading technique. Cascading the P-matrixes of unloaded areas with mass loaded areas, the sensitivity for different sensitive areas was obtained by analyzing the frequency shift. The performance of the sensitivity analysis was confirmed by the measured responses from the silicon dioxide (SiO2) deposited on different sensitive areas of R-SAW resonators. It is shown that the mass sensitivity varies strongly for different sensitive areas, and the optimal sensitive area lies towards the center of the device

Finite Element Modelling of the Effect of Adhesive Z-Connections on the Swelling of a Laminated Wood Composite

This study used finite element analysis (FEA) to model the effects of adhesive Z-connections on the thickness swelling of laminated wood composites exposed to water. We hypothesized that the area density, diameter, and spatial distribution of adhesive Z-connections will influence the ability of Z-connections to restrain thickness swelling of the composites. We tested this hypothesis by modelling a wood composite in ANSYS FEA software v. 17.0 to explore the effect of moisture on the thickness swelling of the wood composite. The results were compared with those obtained experimentally. We then examined the effect of the area density, size (diam.), and spatial distribution of the adhesive Z-connections on the thickness swelling of wood composites. Our results showed a positive correlation between the number of adhesive Z-connections in the composites and restriction of thickness swelling following 72 h of simulated moisture diffusion. Similarly, increasing the size of adhesive Z-connections also restricted thickness swelling. In contrast, different spatial distributions of Z-connections had little effect on restraining thickness swelling. Our modelling approach opens up opportunities for more complex designs of adhesive Z-connections, and also to examine the effect of wood properties, such as permeability, density, and hygroscopic swelling ratios on the thickness swelling of laminated wood composites.Forestry, Faculty ofOther UBCWood Science, Department ofReviewedFacultyResearche

A Novel Particulate Matter 2.5 Sensor Based on Surface Acoustic Wave Technology

Design, fabrication and experiments of a miniature particulate matter (PM) 2.5 sensor based on the surface acoustic wave (SAW) technology were proposed. The sensor contains a virtual impactor (VI) for particle separation, a thermophoretic precipitator (TP) for PM2.5 capture and a SAW sensor chip for PM2.5 mass detection. The separation performance of the VI was evaluated by using the finite element method (FEM) model and the PM2.5 deposition characteristic in the TP was obtained by analyzing the thermophoretic theory. Employing the coupling-of-modes (COM) model, a low loss and high-quality SAW resonator was designed. By virtue of the micro electro mechanical system (MEMS) technology and semiconductor technology, the SAW based PM2.5 sensor detecting probe was fabricated. Then, combining a dual-port SAW oscillator and an air sampler, the experimental platform was set up. Exposing the PM2.5 sensor to the polystyrene latex (PSL) particles in a chamber, the sensor performance was evaluated. The results show that by detecting the PSL particles with a certain diameter of 2 μm, the response of the SAW based PM2.5 sensor is linear, and in accordance with the response of the light scattering based PM2.5 monitor. The developed SAW based PM2.5 sensor has great potential for the application of airborne particle detection

Multidimensional optimization for accelerating light-powered biocatalysis in Rhodopseudomonas palustris

Abstract Background Whole-cell biocatalysis has been exploited to convert a variety of substrates into high-value bulk or chiral fine chemicals. However, the traditional whole-cell biocatalysis typically utilizes the heterotrophic microbes as the biocatalyst, which requires carbohydrates to power the cofactor (ATP, NAD (P)H) regeneration. Results In this study, we sought to harness purple non-sulfur photosynthetic bacterium (PNSB) as the biocatalyst to achieve light-driven cofactor regeneration for cascade biocatalysis. We substantially improved the performance of Rhodopseudomonas palustris-based biocatalysis using a highly active and conditional expression system, blocking the side-reactions, controlling the feeding strategy, and attenuating the light shading effect. Under light-anaerobic conditions, we found that 50 mM ferulic acid could be completely converted to vanillyl alcohol using the recombinant strain with 100% efficiency, and > 99.9% conversion of 50 mM p-coumaric acid to p-hydroxybenzyl alcohol was similarly achieved. Moreover, we examined the isoprenol utilization pathway for pinene synthesis and 92% conversion of 30 mM isoprenol to pinene was obtained. Conclusions Taken together, these results suggested that R. palustris could be a promising host for light-powered biotransformation, which offers an efficient approach for synthesizing value-added chemicals in a green and sustainable manner. Graphical Abstrac