An investigation of methods for improving accuracy of the SpeechWeb interface.

As with many computer speech applications, speech-recognition accuracy is one of main problems of the SpeechWeb system and requires theoretical research. Many approaches for improving speech-recognition accuracy have been carried out and some results have been found, but there is still a far way to go. A new approach, which involves embedding semantic constraints in the syntax of the recognition grammars has been developed by Frost. This report describes an investigation of the new approach and other potential solutions. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2002 .L53. Source: Masters Abstracts International, Volume: 41-04, page: 1111. Adviser: Richard A. Frost. Thesis (M.Sc.)--University of Windsor (Canada), 2002

Aesthetics and the Economic Beauty of Socialism

Some scholars have studied economic beauty with the lack of overall, systematic ideas. Basing on the previous research, this article focuses on the economic beauty and socialist economic beauty, and discusses the concept and characteristics of economic beauty. According to the definition and characteristics of economic beauty, the alienation of economic beauty appears in capitalist economic activities, which is rooted in capitalist production relations, and existed in its economic activities. Socialist economy avoids the problem of alienation of capitalist economic beauty. The value, humanistic care, and belief in truth, goodness and beauty in socialist economy are of great theoretical and practical significance. </p

Review of damage problems of the soft substrate interlayer film

This paper reviews the research progress of soft substrate interlayer film, including the applications and the preparation methods of the soft substrate interlayer film, the testing means of the films’ structure and composition and the researches of the film damage, especially the dynamic damage. As well as the future research directions and applications are put forward

Predicting taxonomic and functional structure of microbial communities in acid mine drainage.

Predicting the dynamics of community composition and functional attributes responding to environmental changes is an essential goal in community ecology but remains a major challenge, particularly in microbial ecology. Here, by targeting a model system with low species richness, we explore the spatial distribution of taxonomic and functional structure of 40 acid mine drainage (AMD) microbial communities across Southeast China profiled by 16S ribosomal RNA pyrosequencing and a comprehensive microarray (GeoChip). Similar environmentally dependent patterns of dominant microbial lineages and key functional genes were observed regardless of the large-scale geographical isolation. Functional and phylogenetic β-diversities were significantly correlated, whereas functional metabolic potentials were strongly influenced by environmental conditions and community taxonomic structure. Using advanced modeling approaches based on artificial neural networks, we successfully predicted the taxonomic and functional dynamics with significantly higher prediction accuracies of metabolic potentials (average Bray-Curtis similarity 87.8) as compared with relative microbial abundances (similarity 66.8), implying that natural AMD microbial assemblages may be better predicted at the functional genes level rather than at taxonomic level. Furthermore, relative metabolic potentials of genes involved in many key ecological functions (for example, nitrogen and phosphate utilization, metals resistance and stress response) were extrapolated to increase under more acidic and metal-rich conditions, indicating a critical strategy of stress adaptation in these extraordinary communities. Collectively, our findings indicate that natural selection rather than geographic distance has a more crucial role in shaping the taxonomic and functional patterns of AMD microbial community that readily predicted by modeling methods and suggest that the model-based approach is essential to better understand natural acidophilic microbial communities

Experimental and numerical investigation of bulging behaviour of hyperelastic textured tubes

The inflation and propagation of a localized instability in elastic tubes shares the same mathematical features with a range of other localization problems, including buckling propagation in long metal tubes under external pressure. Recent research into origami-inspired tubular geometries has suggested that geometric texturing is able to significantly improve metal pipe resistance to propagation buckling failures, with an increase in critical and propagation pressures. This paper aims to investigate whether texturing generates a similar improvement in hyperelastic tubes under axial loading and internal pressure, with elastomer origami structures of recent interest for use as soft actuators and robots. A new fabrication method with 3D printed moulds in a dip process was first developed to enable fabrication of textured tube samples. An experimental study was then conducted on inflated smooth and textured latex tubes, with instability formation observed at a 1 ms resolution. Comparative numerical models with a Mooney–Rivlin material were able to provide a good prediction of experimentally-observed behaviours up to and slightly past the critical pressure and bulge formation. A parametric numerical study is then conducted to show that the number of divisions in the axial direction and circumferential direction have no and modest effects on critical pressure, respectively. The experimental and numerical investigations both showed that the critical pressure of the textured tube was increased compared to the smooth tube, however the degree of increase was a modest 8% and so unlikely to be of significant practical benefit. This work can provide reference and guidelines for future investigations of tubular inflatable origami structures

An improved EfficientNet model and its applications in pneumonia image classification

Since the outburst of COVID-19, the medical system has been facing great challenges due to the explosive growth in detection and treatment needs within a short period. To improve the working efficiency of doctors, an improved EfficientNet model of Convolutional Neural Network (CNN) was proposed and applied for the diagnosis of pneumonia cases and the classification of relevant images in the present study. First, the acquired images of pneumonia cases were divided to determine the zones with target features, and image size was limited to improve the training speed of the network. Meanwhile, reinforcement learning was performed to the input dataset to further improve the training effect of the model. Second, the preprocessed images were inputted into the improved EfficientNet-B4 model. The depth and width of the model, as well as the resolution of the input images, were determined by optimizing the combination coefficient. On this basis, the model was scaled, and then its ability in extracting the features of deep-layer images was strengthened by introducing the attention mechanism. Third, the learning rate was adjusted by using the Adaptive Momentum (ADAM), and the training efficiency of the model was accelerated. Finally, the test set was inputted into the trained model. Results demonstrate that the improved model could detect 98% of patients with pneumonia and 97% of patients without pneumonia. The accuracy rate, precision rate, and sensitivity of the model were generally improved. Lastly, the training and test results of VGGNet, SqueezeNet-Elus, SqueezeNet-Relu, and the improved EfficientNet-B4 models were compared and evaluated. The improved EfficientNet-B4 model achieved the highest comprehensive accuracy rate, reaching 92.95%. The proposed method provides some references to the application of the CNN model in image classification and recognition