The aim of this paper is to review and evaluate models used to assess the market penetration of energy technologies. While there are different models and tools, choosing the appropriate approach for a particular application is very challenging. In this paper, each model is reviewed and discussed extensively and a hierarchy diagram is developed to help choose a model. Market penetration models based on subjective estimation and market survey could be individual-dependent and not reliable for long-term forecasts. Cost estimation, diffusion, and econometric models offer more reliable results both for short- and long-term forecasts. Based on the review, a new combined model was developed and applied to a case study. The combined use of different market penetration models offers more accurate and robust results, as demonstrated in the case study.

Indian Art Music has a huge variety of rāgas. The similarity across rāgas has traditionally been approached from various musicological viewpoints. This work aims at discovering structural similarities among renditions of rāgas using a data-driven approach. Starting from melodic contours, we obtain the descriptive note-level transcription of each rendition. Repetitive note patterns of variable and fixed lengths are derived using stochastic models. We propose a latent variable approach for raga distinction based on statistics of these patterns. The posterior probability of the latent variable is shown to capture similarities across raga renditions. We show that it is possible to visualize the similarities in a low-dimensional embedded space. Experiments show that it is possible to compare and contrast relations and distances between ragas in the embedded space with the musicological knowledge of the same for both Hindustani and Carnatic music forms. The proposed approach also shows robustness to duration of rendition.

Predominant melody offers a complete representation of melodic contours of heterophonic Indian Art Music (IAM). A compact representation of melodic contours while preserving raga characteristics is proposed. Such representations have applications in music transcription, analysis, and synthesis. Contours are quantized on a pitch-time grid after normalizing critical points with tonic and rhythmic pulse period estimates. Non-uniform quantization intervals are selected from pitch and time scales prevalent in IAM, while accommodating pitch and inter-note-interval variations on pitch-time grid. An evaluation of quantized-reconstructed contours through listening tests by trained musicians shows raga preserving capabilities of the proposed approach in spite of alterations in contour shapes.

The growth in bitumen and synthetic crude oil (SCO) production in the Canadian oil sands industry has superseded pipeline capacity growth in recent years, leading to the increased interest in the transport of crude oil by rail to desired markets. However, the specific techno-economic parameters that facilitate increased competitiveness of either transportation mode against the other is seldom addressed in the existing literature. This paper involves the development of a rail and pipeline techno-economic transport model, which is used to ascertain the transportation cost of both options for a market distance range of 1-3000 km and a production scale of 100,000-750,000 barrels per day (bpd). The transportation cost for either option is highly sensitive to the market distance, transportation scale and crude grade being transported; however, pipelines are generally more competitive for large transportation scales, while the cost-effectiveness of rail transport is realized particularly at smaller transportation scales. In general, pipelines are cost efficient for the transportation of crude oil in the majority of scenarios investigated. Rail can be more economical than pipeline under certain conditions. The use of insulated rail cars for the transport of raw bitumen is the area with greatest potential for cost competitiveness against pipelines. (C) 2017 Elsevier Ltd. All rights reserved.

The energy sector is the largest contributor to gross domestic product (GDP), income, employment, and government revenue in both developing and developed nations. But the energy sector has a significant environmental footprint due to greenhouse gas (GHG) emissions. Efficient production, conversion, and use of energy resources are key factors for reducing the environmental footprint. Hence it is necessary to understand energy flows from both the supply and the demand sides. Most energy analyses focus on improving energy efficiency broadly without considering the aggregate energy flow. We developed Sankey diagrams that map energy flow for both the demand and supply sides for the province of Alberta, Canada. The diagrams will help policy/decision makers, researchers, and others to understand energy flow from reserves through to final energy end uses for primary and secondary fuels in the five main energy demand sectors in Alberta: residential, commercial, industrial, agricultural, and transportation. The Sankey diagrams created for this study show total energy consumption, useful energy, and energy intensities of various end-use devices. The Long-range Energy Alternatives Planning System (LEAP) model is used in this study. The model showed that Alberta's total input energy in the five demand sectors was 189 PJ, 186 PJ, 828.5 PJ, 398 PJ, and 50.83 PJ, respectively. On the supply side, the total energy input and output were found to be 644.84 PJ and 239 PJ, respectively. These results, along with the associated energy flows were depicted pictorially using Sankey diagrams. The Sankey diagrams reveal the current efficiencies within various end-use sectors and could help identify options for improving energy efficiency in order to reduce GHG emissions. (C) 2014 Elsevier Ltd. All rights reserved.