The Translocal Event and the Polyrhythmic Diagram

This thesis identifies and analyses the key creative protocols in translocal performance practice, and ends with suggestions for new forms of transversal live and mediated performance practice, informed by theory. It argues that ontologies of emergence in dynamic systems nourish contemporary practice in the digital arts. Feedback in self-organised, recursive systems and organisms elicit change, and change transforms. The arguments trace concepts from chaos and complexity theory to virtual multiplicity, relationality, intuition and individuation (in the work of Bergson, Deleuze, Guattari, Simondon, Massumi, and other process theorists). It then examines the intersection of methodologies in philosophy, science and art and the radical contingencies implicit in the technicity of real-time, collaborative composition. Simultaneous forces or tendencies such as perception/memory, content/ expression and instinct/intellect produce composites (experience, meaning, and intuition- respectively) that affect the sensation of interplay. The translocal event is itself a diagram - an interstice between the forces of the local and the global, between the tendencies of the individual and the collective. The translocal is a point of reference for exploring the distribution of affect, parameters of control and emergent aesthetics. Translocal interplay, enabled by digital technologies and network protocols, is ontogenetic and autopoietic; diagrammatic and synaesthetic; intuitive and transductive. KeyWorx is a software application developed for realtime, distributed, multimodal media processing. As a technological tool created by artists, KeyWorx supports this intuitive type of creative experience: a real-time, translocal “jamming” that transduces the lived experience of a “biogram,” a synaesthetic hinge-dimension. The emerging aesthetics are processual – intuitive, diagrammatic and transversal

When Moneyball Meets the Beautiful Game: A Predictive Analytics Approach to Exploring Key Drivers for Soccer Player Valuation

To measure the market value of a professional soccer (i.e., association football) player is of great interest to soccer clubs. Several gaps emerge from the existing soccer transfer market research. Economics literature only tests the underlying hypotheses between a player’s market value or wage and a few economic factors. Finance literature provides very theoretical pricing frameworks. Sports science literature uncovers numerous pertinent attributes and skills but gives limited insights into valuation practice. The overarching research question of this work is: what are the key drivers of player valuation in the soccer transfer market? To lay the theoretical foundations of player valuation, this work synthesizes the literature in market efficiency and equilibrium conditions, pricing theories and risk premium, and sports science. Predictive analytics is the primary methodology in conjunction with open-source data and exploratory analysis. Several machine learning algorithms are evaluated based on the trade-offs between predictive accuracy and model interpretability. XGBoost, the best model for player valuation, yields the lowest RMSE and the highest adjusted R2. SHAP values identify the most important features in the best model both at a collective level and at an individual level. This work shows a handful of fundamental economic and risk factors have more substantial effect on player valuation than a large number of sports science factors. Within sports science factors, general physiological and psychological attributes appear to be more important than soccer-specific skills. Theoretically, this work proposes a conceptual framework for soccer player valuation that unifies sports business research and sports science research. Empirically, the predictive analytics methodology deepens our understanding of the value drivers of soccer players. Practically, this work enhances transparency and interpretability in the valuation process and could be extended into a player recommender framework for talent scouting. In summary, this work has demonstrated that the application of analytics can improve decision-making efficiency in player acquisition and profitability of soccer clubs

Occupancy driven supervisory control of indoor environment systems to minimise energy consumption of airport terminal building

A very economical way of reducing the operational energy consumed by large commercial buildings such as an airport terminal is the automatic control of its active energy systems. Such control can adjust the indoor environment systems setpoints to satisfy comfort during occupancy or when unoccupied, initiate energy conservation setpoints and if necessary, shut down part of the building systems. Adjusting energy control setpoints manually in large commercial buildings can be a nightmare for facility managers. Incidentally for such buildings, occupancy based control strategies are not achieved through the use of conventional controllers alone. This research, therefore, investigated the potential of using a high-level control system in airport terminal building. The study presents the evolution of a novel fuzzy rule-based supervisory controller, which intelligently establishes comfort setpoints based on flow of passenger through the airport as well as variable external environmental conditions. The inputs to the supervisory controller include: the time schedule of the arriving and departing passenger planes; the expected number of passengers; zone daylight illuminance levels; and external temperature. The outputs from the supervisory controller are the low-level controllers internal setpoint profile for thermal comfort, visual comfort and indoor air quality. Specifically, this thesis makes contribution to knowledge in the following ways: It utilised artificial intelligence to develop a novel fuzzy rule-based, energy-saving supervisory controller that is able to establish acceptable indoor environmental quality for airport terminals based on occupancy schedules and ambient conditions. It presents a unique methodology of designing a supervisory controller using expert knowledge of an airport s indoor environment systems through MATLAB/Simulink platform with the controller s performance evaluated in both MATLAB and EnergyPlus simulation engine. Using energy conservation strategies (setbacks and switch-offs), the pro-posed supervisory control system was shown to be capable of reducing the energy consumed in the Manchester Airport terminal building by up to 40-50% in winter and by 21-27% in summer. It demonstrates that if a 45 minutes passenger processing time is aimed for instead of the 60 minutes standard time suggested by ICAO, energy consumption is significantly reduced (with less carbon emission) in winter particularly. The potential of the fuzzy rule-based supervisory controller to optimise comfort with minimal energy based on variation in occupancy and external conditions was demonstrated through this research. The systematic approach adopted, including the use of artificial intelligence to design supervisory controllers, can be extended to other large buildings which have variable but predictable occupancy patterns

Methodology to predict construction contractors’ performance using non-price measures

Despite being one of the largest industry sectors in the world, construction continues to suffer from underperformance. Contractors are the driving force behind built assets, and selecting high-performing contractors is crucial to the success of construction projects. However, the industry lacks a systematic and purpose-driven method of assessing contractors’ performance using objective metrics. Furthermore, contractors do not have a systematic way to gauge their own performance in the pursuit of continuous improvement. Although there are numerous approaches to the measurement of contractors’ performance, the literature suggests that most are complicated and highly dependent on data that are difficult to attain. The research presented in this thesis addresses this knowledge gap by creating a model for predicting construction contractors’ performance based on directly attributable measures that are quantitatively measurable and easily accessible. The findings of this research make a number of contributions to theory and practice. The developed performance model—the Contractors’ Performance Index (CPIx) provides a performance score based on seven non-price CMoPs. As the CPIx is based on factors that are within the control of the contractor, it provides a fair and independent assessment of performance that is not influenced by other factors. In an industry significantly driven by pricebased decisions that are solely based on non-price measures, the CPIx shifts the focus towards other aspects such as quality, health and safety, sustainability and productivity when evaluating performance, leaving price based measures for commercial considerations. Contractors can use the CPIx to self-evaluate their levels of project and organisational performance. If implemented as a sector-based performance evaluator, it can then be used to develop industry benchmarks for different categories of construction. The CPIx is presented as a prototype mobile application that can be conveniently used by various stakeholders to track performance within the construction industry

Short-term forecast techniques for energy management systems in microgrid applications

A Dissertation Submitted in Partial Fulfilment of the Requirements for the Degree of Doctor of Philosophy in Sustainable Energy Science and Engineering of the Nelson Mandela African Institution of Science and TechnologyIn the 2015 Paris Agreement, 195 countries adopted a global climate agreement to limit the global average temperature rise to less than 2°C. Achieving the set targets involves increasing energy efficiency and embracing cleaner energy solutions. Although advances in computing and Internet of Things (IoT) technologies have been made, there is limited scientific research work in this arena that tackles the challenges of implementing low-cost IoT-based Energy Management System (EMS) with energy forecast and user engagement for adoption by a layman both in off-grid or microgrid tied to a weak grid. This study proposes an EMS approach for short-term forecast and monitoring for hybrid microgrids in emerging countries. This is done by addressing typical submodules of EMS namely: load forecast, blackout forecast, and energy monitoring module. A short-term load forecast model framework consisting of a hybrid feature selection and prediction model was developed. Prediction error performance evaluation of the developed model was done by varying input predictors and using the principal subset features to perform supervised training of 20 different conventional prediction models and their hybrid variants. The proposed principal k-features subset union approach registered low error performance values than standard feature selection methods when it was used with the ‘linear Support Vector Machine (SVM)’ prediction model for load forecast. The hybrid regression model formed from a fusion of the best 2 models (‘linearSVM’ and ‘cubicSVM’) showed improved prediction performance than the individual regression models with a reduction in Mean Absolute Error (MAE) by 5.4%. In the case of the EMS blackout prediction aspect, a hybrid Adaptive Similar Day (ASD) and Random Forest (RF) model for short-term power outage prediction was proposed that predicted accurately almost half of the blackouts (49.16%), thereby performing slightly better than the stand-alone RF (32.23%), and ASD (46.57%) models. Additionally, a low-cost EMS smart meter was developed to realize the implemented energy forecast and offer user engagement through monitoring and control of the microgrid towards the goal of increasing energy efficiency