Cold emulsion polyvinyl bindability criteria

The purpose of this study is to evaluate new types of materials and technologies in the fast-changing bindery industry. An optimum binding method (OBM) is being determined by using different brands of cold emulsion polyvinyl acetate, different adhesive, binding methods, spine preparation, and papers. Testing is performed with a combination of materials and technologies to determine the actual binding strength and performance of adhesives. Results of the tests were analyzed statistically by Analysis of Variance (ANOVA), graphing, percent of change and ranking of values to find the optimum. The results of this study show some relationships of the factors, adhesives, binding styles, paper, and grain direction of paper when tested for pounds of pull using various types of tests; pagepull, cornerpull, subwaypull, pagepull after Universal Book Tester (UBT), and pagepull after tumble. The spine preparation was shown to be the most critical factor for the strength of the book. Other critical factors for strength were the kind and grain direction of the paper. The OBM was found using the perfo adhesive binding method, adhesive B, and uncoated paper with the grain direction short. There were no 8, 16 or 32 page signatures used for the testing. Only 4 page signatures were used

A Comparison of Audio Models for Virtual Reality Video

This paper investigates the relationship between audio models for Virtual Reality (VR) video with respect to the senses of immersion and realism that each model delivers. Mono, Stereo, 5.1 Surround Sound, and a Virtual Spatialised Position configuration was developed for testing in a VR music video and evaluated with a user study. Participants experienced the VR video with these differing audio models as accompaniment a total of four times. Qualitative and quantitative data were recorded to evaluate user experience. The results indicate that no statistical significance was present between the four models in relation to immersion or realism, suggesting that complex audio renderings are not always necessary for effective user experience

Collaborative Artificial Intelligence in Music Production

The use of technology has revolutionized the process of music composition, recording, and production in the last 30 years. One fusion of technology and music that has been longstanding is the use of artificial intelligence in the process of music composition. However, much less attention has been given to the application of AI in the process of collaboratively composing and producing a piece of recorded music. The aim of this project is to explore such use of artificial intelligence in music production. The research presented here includes discussion of an auto ethnographic study of the interactions between songwriters, with the intention that these can be used to model the collaborative process and that a computational system could be trained using this information. The research indicated that there were repeated patterns that occurred in relation to the interactions of the participating songwriters

Flexible fiberoptic pericardioscopy for the diagnosis of pericardial disease

Pericardiocentesis provides an etiologic diagnosis for pericardial effusions approximately 25% of the time. In seven patients with evidence of a large pericardial effusion of unknown origin without cardiac tamponade, a flexible fiberoptic bronchoscope was inserted through a subxiphoid incision after the effusion was drained. Pericardioscopy allowed visualization of all pericardial surfaces and made it possible to perform selective biopsy not limited to a subxiphoid window. It is a safe procedure that can permit distinction among benign, malignant and tuberculous origins of pericardial effusion

Emotion, Content & Context in Sound and Music

Computer game sound is particularly dependent upon the use of both sound artefacts and music. Sound and music are media rich in information. Audio and music processing can be approached from a range of perspectives which may or may not consider the meaning and purpose of this information. Computer music and digital audio are being advanced through investigations into emotion, content analysis, and context, and this chapter attempts to highlight the value of considering the information content present in sound, the context of the user being exposed to the sound, and the emotional reactions and interactions that are possible between the user and game sound. We demonstrate that by analysing the information present within media and considering the applications and purpose of a particular type of information, developers can improve user experiences and reduce overheads while creating more suitable, efficient applications. Some illustrated examples of our research projects that employ these theories are provided. Although the examples of research and development applications are not always examples from computer game sound, they can be related back to computer games. We aim to stimulate the reader’s imagination and thought in these areas, rather than attempt to drive the reader down one particular path

High-Level Analysis of Audio Features for Identifying Emotional Valence in Human Singing

Emotional analysis continues to be a topic that receives much attention in the audio and music community. The potential to link together human affective state and the emotional content or intention of musical audio has a variety of application areas in fields such as improving user experience of digital music libraries and music therapy. Less work has been directed into the emotional analysis of human acapella singing. Recently, the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) was released, which includes emotionally validated human singing samples. In this work, we apply established audio analysis features to determine if these can be used to detect underlying emotional valence in human singing. Results indicate that the short-term audio features of: energy; spectral centroid (mean); spectral centroid (spread); spectral entropy; spectral flux; spectral rolloff; and fundamental frequency can be useful predictors of emotion, although their efficacy is not consistent across positive and negative emotions

Practical Large-Scale Network Design with Variable Costs for Links and Switches

This paper considers communication network design problems that arise in the real world, with large numbers of nodes and link and switch costs dependent upon their traffic capacity. Such costs, in turn, depend upon network topology so are not fixed at the start of, or through, any optimisation process. Realistic topological restrictions are also discussed. The limitations of conventional approaches – both constructive and search based – are noted and the requirements of practical optimisation methods explored. Two workable approaches to network design - one an established local search variant, another a more novel geometric approach - are introduced. Five different algorithms, ranging from exhaustive search to fast heuristic are compared with experimental results given in conclusion

Doppler echocardiography assessment of impaired left ventricular filling in patients with right ventricular pressure overload due to primary pulmonary hypertension

In patients with primary pulmonary hypertension, competition between the right and left ventricles for the limited pericardial space results in distortion of left ventricular geometry reflected in displacement of the ventricular septum toward the left ventricular cavity. Left ventricular shape is most dramatically deranged at end-systole and early diastole, suggesting the possibility that the distribution of left ventricular diastolic filling might be altered. To investigate this hypothesis, nine patients with primary pulmonary hypertension and nine normal individuals were studied with echocardiographic techniques. Left ventricular isovolumic relaxation time was significantly prolonged in patients with primary pulmonary hypertension by comparison with normal individuals (129 ± 36 versus 53 ± 9 ms, p < 0.005) and the fraction of the transmitral flow velocity integral occurring in the first half of diastole was significantly less than in normal individuals (38 ± 14% versus 70 ± 9%, p < 0.005). Measurement of fractional changes in short-axis left ventricular cavity area similarly demonstrated that in patients with primary pulmonary hypertension fractional early diastolic cavity expansion (32 ± 11%) was significantly less than in normal individuals (78 ± 9%, p < 0.005).In patients with primary pulmonary hypertension, the ventricular septum was abnormally flattened toward the left ventricular cavity at end-systole (normalized septal curvature 0.04 ± 0.19) and remained that way throughout early diastolic filling but returned toward normal at end-diastole (normalized septal curvature 0.68 ± 0.19, p < 0.005). Thus, in patients with primary pulmonary hypertension end-systolic and early diastolic deformation of the left ventricle by septal flattening toward the left ventricular cavity is associated with relative underfilling of the left ventricle in early diastole and redistribution of left ventricular filling into late diastole. The reliance on late diastolic filling and atrial systole to maintain left ventricular preload in primary pulmonary hypertension may have important implications for the use of vasodilators in this disease

Supervised machine learning for audio emotion recognition

The field of Music Emotion Recognition has become and established research sub-domain of Music Information Retrieval. Less attention has been directed towards the counterpart domain of Audio Emotion Recognition, which focuses upon detection of emotional stimuli resulting from non-musical sound. By better understanding how sounds provoke emotional responses in an audience, it may be possible to enhance the work of sound designers. The work in this paper uses the International Affective Digital Sounds set. A total of 76 features are extracted from the sounds, spanning the time and frequency domains. The features are then subjected to an initial analysis to determine what level of similarity exists between pairs of features measured using Pearson’s r correlation coefficient before being used as inputs to a multiple regression model to determine their weighting and relative importance. The features are then used as the input to two machine learning approaches: regression modelling and artificial neural networks in order to determine their ability to predict the emotional dimensions of arousal and valence. It was found that a small number of strong correlations exist between the features and that a greater number of features contribute significantly to the predictive power of emotional valence, rather than arousal. Shallow neural networks perform significantly better than a range of regression models and the best performing networks were able to account for 64.4% of the variance in prediction of arousal and 65.4% in the case of valence. These findings are a major improvement over those encountered in the literature. Several extensions of this research are discussed, including work related to improving data sets as well as the modelling processes