Resonance : a music director’s theomusicological study of ‘praise’ and ‘glory’ in a selection of four liturgical music excerpts

Some of my most powerful spiritual experiences have come from the splendorous and sublime sounding hymns performed by a choir and church organ at the traditional Anglican church I’ve attended since I was very young. In the later stage of my life, my pursuit of education in the field of engineering caused me to move to Australia where I regularly attended a contemporary evangelical church and subsequently became a music director in the faith community. This environmental and cultural shift altered my perception and musical experiences of Christian music and led me to enquire about the relationship between Christian liturgy and church music. Throughout history church musicians and composers have synthesised the theological, congregational, cultural and musical aspects of church liturgy. Many great composers have taken into account the conditions surrounding the process of sacred composition and arrangement of music to enhance the experience of religious ecstasy – they sought resonances with Christian values and beliefs to draw congregational participation into the light of praising and glorifying God. As a music director in an evangelical church this aspiration has become one I share. I hope to identify and define the qualities of these resonances that have been successful and apply them to my own practice. Introduction and Structure of the Thesis In this study I will examine four purposively selected excerpts of Christian church vocal music combining theomusicological and semiotic analysis to help identify guidelines that might be useful in my practice as a church music director. The four musical excerpts have been selected based upon their sustained musical and theological impact over time, and their ability to affect ecstatic responses from congregations. This thesis documents a personal journey through analysis of music and uses a context that draws upon ethno-musicological, theological and semiotic tools that lead to a preliminary framework and principles which can then be applied to the identified qualities of resonance in church music today. The thesis is comprised of four parts. Part 1 presents a literature study on the relationship between sacred music, the effects of religious ecstasy and the Christian church. Multiple lenses on this phenomenon are drawn from the viewpoints of prominent western church historians, Biblical theologians, and philosophers. The literature study continues in Part 2, where the role of embodiment is examined from the current perspective of cognitive learning environments. This study offers a platform for a critical reflection on two distinctive musical liturgical systems that have treated differently the notion of embodied understanding amidst a shifting church paradigm. This allows an in-depth theological and philosophical understanding of the liturgical conditions around sacred music-making that relates to the monistic and dualistic body/mind. Part 3 involves undertaking a theomusicological methodology that utilises creative case studies of four purposively selected spiritual pieces. A semiotic study focuses on specific sections of sacred vocal works that express the notions of ‘praise’ and ‘glorification’, particularly in relation to these effects,which combine an analysis of theological perspectives around religious ecstasy and particular spiritual themes. Part 4 presents the critiques and findings gathered from the study that incorporate theoretical and technological means to analyse the purposive selected musical artefact, particularly with the sonic narratives expressing notions of ‘Praise' and 'Glory’. The musical findings are further discussed in relation to the notion of resonance, and then a conceptual framework for the role of contemporary musicdirector is proposed. The musical and Christian terminologies used in the thesis are explained in the glossary, and the appendices includes tables illustrating the musical findings, conducted surveys, written musical analyses and audio examples of selected sacred pieces available on the enclosed compact disc.

Selection of solar energy for green building using superiority and inferiority multi-criteria ranking (SIR) method

Green energy is one of the key factors, driving down electricity bill and zero carbon emission generating electricity to green building. However, the climate change and environmental policies are accelerating people to use renewable energy instead of coal-fired (convention type) energy for green building that energy is not environmental friendly. Therefore, solar energy is one of the clean energy solving environmental impact and paying less in electricity fee. The method of solar energy is collecting sun from solar array and saves in battery from which provides necessary electricity to whole house with zero carbon emission. However, in the market a lot of solar arrays suppliers, the aims of this paper attempted to use superiority and inferiority multi-criteria ranking (SIR) method with 13 constraints establishing I-flows and S-flows matrices to evaluate four alternatives solar energies and determining which alternative is the best, providing power to sustainable building. Furthermore, SIR is well-known structured approach of multi-criteria decision support tools and gradually used in construction and building. The outcome of this paper significantly gives an indication to user selecting solar energy.

Model selection and error estimation

We study model selection strategies based on penalized empirical loss minimization. We point out a tight relationship between error estimation and data-based complexity penalization: any good error estimate may be converted into a data-based penalty function and the performance of the estimate is governed by the quality of the error estimate. We consider several penalty functions, involving error estimates on independent test data, empirical VC dimension, empirical VC entropy, and margin-based quantities. We also consider the maximal difference between the error on the first half of the training data and the second half, and the expected maximal discrepancy, a closely related capacity estimate that can be calculated by Monte Carlo integration. Maximal discrepancy penalty functions are appealing for pattern classification problems, since their computation is equivalent to empirical risk minimization over the training data with some labels flipped.

Margin-adaptive model selection in statistical learning

A classical condition for fast learning rates is the margin condition, first introduced by Mammen and Tsybakov. We tackle in this paper the problem of adaptivity to this condition in the context of model selection, in a general learning framework. Actually, we consider a weaker version of this condition that allows one to take into account that learning within a small model can be much easier than within a large one. Requiring this “strong margin adaptivity” makes the model selection problem more challenging. We first prove, in a general framework, that some penalization procedures (including local Rademacher complexities) exhibit this adaptivity when the models are nested. Contrary to previous results, this holds with penalties that only depend on the data. Our second main result is that strong margin adaptivity is not always possible when the models are not nested: for every model selection procedure (even a randomized one), there is a problem for which it does not demonstrate strong margin adaptivity.

SwarmPS: Rapid, semi-automated single particle selection software

Single particle analysis (SPA) coupled with high-resolution electron cryo-microscopy is emerging as a powerful technique for the structure determination of membrane protein complexes and soluble macromolecular assemblies. Current estimates suggest that ∼104–105 particle projections are required to attain a 3 Å resolution 3D reconstruction (symmetry dependent). Selecting this number of molecular projections differing in size, shape and symmetry is a rate-limiting step for the automation of 3D image reconstruction. Here, we present SwarmPS, a feature rich GUI based software package to manage large scale, semi-automated particle picking projects. The software provides cross-correlation and edge-detection algorithms. Algorithm-specific parameters are transparently and automatically determined through user interaction with the image, rather than by trial and error. Other features include multiple image handling (∼102), local and global particle selection options, interactive image freezing, automatic particle centering, and full manual override to correct false positives and negatives. SwarmPS is user friendly, flexible, extensible, fast, and capable of exporting boxed out projection images, or particle coordinates, compatible with downstream image processing suites.

The influence of cultural selection on strategic communication

Strategic communication is held to be a key process by which organisations respond to environmental uncertainty. In the received view articulated in the literatures of organisational communication and public relations, strategic communication results from collaborative efforts by organisational members to create shared understanding about environmental uncertainty and, as a result of this collective understanding, formulate appropriate communication responses. In this study, I explore how such collaborative efforts towards the development of strategic communication are derived from, and bounded by, culturally shared values and assumptions. Study of the influences of an organisation‟s culture on the formulation of strategic communication is a fundamental conceptual challenge for public relations and, to date, a largely unaddressed area of research. This thesis responds to this challenge by describing a key property of organisational culture – the action of cultural selection (Durham, 1992). I integrate this property of cultural selection to extend and refine the descriptive range of Weick‟s (1969, 1979) classic sociocultural model of organizing. From this integration I propose a new model, the Cultural Selection of Strategic Communication (CSSC). Underpinning the CSSC model is the central proposition that because of the action of cultural selection during organizing processes, the inherently conservative properties of an organisation‟s culture constrain development of effective strategic communication in ways that may be unrelated to the outcomes of “environmental scanning” and other monitoring functions heralded by the public relations literature as central to organisational adaptation. Thus, by examining the development of strategic communication, I describe a central conservative influence on the social ecology of organisations. This research also responds to Butschi and Steyn‟s (2006) call for the development of theory focusing on strategic communication as well as Grunig (2006) and Sriramesh‟s (2007) call for research to further understand the role of culture in public relations practice. In keeping with the explorative and descriptive goals of this study, I employ organisational ethnography to examine the influence of cultural selection on the development of strategic communication. In this methodological approach, I use the technique of progressive contextualisation to compare data from two related but distinct cultural settings. This approach provides a range of descriptive opportunities to permit a deeper understanding of the work of cultural selection. Findings of this study propose that culture, operating as a system of shared and socially transmitted social knowledge, acts through the property of cultural selection to influence decision making, and decrease conceptual variation within a group. The findings support the view that strategic communication, as a cultural product derived from the influence of cultural selection, is an essential feature to understand the social ecology of an organisation.

The importance of organisational culture to improve a project selection process : case of public infrastructure project in Indonesia

Project selection is a decision-making process that is not merely influenced by technical aspects but also by the people who involved in the process. Organisational culture is described as a set of values and norms that are shared by people within the organisation that affects the way they interact with each other and with stakeholders from outside the organisation. The aim of this paper is to emphasize the importance of organisational culture on improving the quality of decisions in the project selection process, in addition to the influence of technical aspects of a project. The discussion is based on an extensive literature review and, as such, represents the first part of a research agenda investigating the impact of organisational culture on the project selection process applicable specifically to road infrastructure contracts. Four existing models of organisational culture (Denison 1990; Cameron and Quinn 2006; Hofstede 2001; Glaser et al 1987) are discussed and reviewed in view of their use in the larger research project to investigate the impact of culture on identified critical elements of decision-making. An understating of the way organisational culture impacts on project selection will increase the likelihood in future of relevant government departments selecting projects that achieve their stated organisational goals.

User-representative feature selection for keystroke dynamics

Continuous user authentication with keystroke dynamics uses characters sequences as features. Since users can type characters in any order, it is imperative to find character sequences (n-graphs) that are representative of user typing behavior. The contemporary feature selection approaches do not guarantee selecting frequently-typed features which may cause less accurate statistical user-representation. Furthermore, the selected features do not inherently reflect user typing behavior. We propose four statistical based feature selection techniques that mitigate limitations of existing approaches. The first technique selects the most frequently occurring features. The other three consider different user typing behaviors by selecting: n-graphs that are typed quickly; n-graphs that are typed with consistent time; and n-graphs that have large time variance among users. We use Gunetti’s keystroke dataset and k-means clustering algorithm for our experiments. The results show that among the proposed techniques, the most-frequent feature selection technique can effectively find user representative features. We further substantiate our results by comparing the most-frequent feature selection technique with three existing approaches (popular Italian words, common n-graphs, and least frequent ngraphs). We find that it performs better than the existing approaches after selecting a certain number of most-frequent n-graphs.