Economic and environmental impacts of collecting waste cooking oil for use as biodiesel under a localized strategy

Some of the vital aspects in the diffusion of renewable energies are the cost of producing the energy, as well as the environmental impacts associated with its lifecycle. As petroleum based energy becomes increasingly costly, alternatives will be relied upon to meet the ever increasing energy demand. Biofuels, and biodiesel in particular, could be a near term solution for providing a transitional fuel to meet the energy demand of the transportation sector. However, the costs of biodiesel, as well as perceptions of a negative energy balance are hindering its widespread adoption. Using waste cooking oil (WCO) can reduce the cost of raw materials necessary for producing biodiesel, when compared to traditional sources, and by collecting and using biodiesel locally, its cost can be further reduced. This research involves the design and development of a simulation model to analyze the costs and emissions associated with waste cooking oil collection for the local, or decentralized, production and use of biodiesel. A case study for the food and beverage industry is investigated. A series of simulation experiments was used to evaluate different scenarios for utilizing the unexploited capacity of a local food and beverage distribution network for the collection of waste cooking oils. The economic and environmental costs associated with collecting WCO were compared to the economic and environmental savings from using biodiesel, the impacts of such operation upon service level are also investigated. Based on the local food and beverage network used to construct the model parameters, biodiesel production from WCO on a localized scale has positive impacts to both cost and emissions without sacrificing customer service

Is It Benign or Is It a Pariah? Empirical Evidence for the Impact of the Common Myna (Acridotheres tristis) on Australian Birds

There is widespread concern over the impact of introduced species on biodiversity, but the magnitude of these impacts can be variable. Understanding the impact of an introduced species is essential for effective management. However, empirical evidence of the impact of an introduced species can be difficult to obtain, especially when the impact is through competition. Change in species abundance is often slow and gradual, coinciding with environmental change. As a result, negative impacts on native species through competition are poorly documented. An example of the difficulties associated with obtaining empirical evidence of impact due to competition comes from work on the Common Myna (Acridotheres tristis). The species is listed in the World's top 100 worst invaders, despite a lack of empirical evidence of its negative impacts on native species. We assessed the impact of the Common Myna on native bird abundance, using long-term data both pre and post its invasion. At the outset of our investigation, we postulated that Common Myna establishment would negatively affect the abundance of other cavity-nesting species and bird species that are smaller than it. We found a negative relationship between the establishment of the Common Myna and the long-term abundance of three cavity-nesting species (Sulphur-crested Cockatoo, Crimson Rosella, Laughing Kookaburra) and eight small bird species (Striated Paradoxes, Rufous Whistler, Willie Wagtail, Grey Fantail, Magpie-lark, House Sparrow, Silvereye, Common Blackbird). To the best of our knowledge, this finding has never previously been demonstrated at the population level. We discuss the key elements of our success in finding empirical evidence of a species impact and the implications for prioritisation of introduced species for management. Specifically, prioritization of the Common Myna for management over other species still remains a contentious issue.This work is supported in part by the Invasive Animals Cooperative Research Centre and the Australian National University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Single live-cell imaging for systems biology

Understanding how mammalian cells function requires a dynamic perspective. However, due to the complexity of signalling networks these non-linear systems can easily elude human intuition. The central aim of systems biology is to improve our understanding of the temporal complexity of cell signalling pathways, using a combination of experimental and computational approaches. Live cell imaging and computational modelling are compatible techniques which allow quantitative analysis of cell signalling pathway dynamics. Non-invasive imaging techniques, based on the use of various luciferases and fluorescent proteins, trace cellular events such as gene expression, protein-protein interactions and protein localisation in cells. By employing a number of markers in a single assay, multiple parameters can be measured simultaneously in the same cell. Following acquisition using specialised microscopy, analysis of multi-parameter time-lapse images facilitates the identification of important qualitative and quantitative relationships – linking intracellular signalling, gene expression and cell fate. Improvements in reporter genes coupled with significant advances in detector technologies, are now allowing us to image gene expression non-invasively in individual living cells. These methods are providing remarkable insights into the dynamics of gene expression during complex processes, such as the cell cycle and the responses of cells to hormones, growth factors and nutrients. On a larger scale, dynamics of gene expression may also be monitored in living organisms. This new technology will greatly assist attempts to decipher the complex behaviours exhibited by biological signalling networks, for instance the ability to integrate multiple input signals over time, and generate specific outputs

Modelling plausible scenarios for the Omicron SARS-CoV-2 variant from early-stage surveillance

In this paper we used an adapted version of an existing simulation model of SARS-CoV-2 transmission in Scotland to investigate the rise of the Omicron variant of concern, in order to evaluate plausible scenarios for transmission advantage and vaccine immune escape relative to the Delta variant. We also explored possible outcomes of different levels of imposed non-pharmaceutical intervention. The initial results of these scenarios were used to inform the Scottish Government in the early outbreak stages of the Omicron variant. We use an explicitly spatial agent-based simulation model combined with spatially fine-grained COVID-19 observation data from Public Health Scotland. Using the model with parameters fit over the Delta variant epidemic, some initial assumptions about Omicron transmission advantage and vaccine escape, and a simple growth rate fitting procedure, we were able to capture the initial outbreak dynamics for Omicron. We also find the modelled dynamics hold up to retrospective scrutiny. We found that the modelled imposition of extra non-pharmaceutical interventions planned by the Scottish Government at the time would likely have little effect in light of the transmission advantage held by the Omicron variant and the fact that the planned interventions would have occurred too late in the outbreak's trajectory. Finally, we found that any assumptions made about the projected distribution of vaccines in the model population had little bearing on the outcome, in terms of outbreak size and timing, rather that the detailed landscape of immunity prior to the outbreak was of far greater importance

Distributional Collision Modeling for Monte Carlo Simulations

Abstract. In this paper we present the initial results in our development of Distributional DSMC (DDSMC) methods. By modifying Nanbu's method to allow distributed velocities, we have shown that DSMC methods are not limited to convergence in probability measure alone, but can achieve strong convergence for L 1 solutions of the Boltzmann equation and pointwise convergence for bounded solutions. We also present an initial attempt at a general distributional method and apply these methods to the Bobylev, Krook, and Wu space homogeneous solution of the Boltzmann equation