Determining gas sampling timelines for estimating emissions in small chamber incubation experiments

A laboratory experiment was set up in small chambers for monitoring greenhouse gas emissions and determining the most suitable time for sampling. A six-treatment experiment was conducted, including a one week pre-incubation and a week for incubation. Timelines for sampling were 1, 2, 3, 6 and 24 hours after closing the lid of the incubation chambers. Variation in greenhouse gas fluxes was high due to the time of sampling. The rates of gas emissions increased in first three hours and decreased afterward. The rates of greenhouse gas emissions at 3 hours after closing lids was close to the mean for the 24-h period.

Long-term experiments with an adaptive spherical view representation for navigation in changing environments

Real-world environments such as houses and offices change over time, meaning that a mobile robot’s map will become out of date. In this work, we introduce a method to update the reference views in a hybrid metrictopological map so that a mobile robot can continue to localize itself in a changing environment. The updating mechanism, based on the multi-store model of human memory, incorporates a spherical metric representation of the observed visual features for each node in the map, which enables the robot to estimate its heading and navigate using multi-view geometry, as well as representing the local 3D geometry of the environment. A series of experiments demonstrate the persistence performance of the proposed system in real changing environments, including analysis of the long-term stability.

Examining the cost efficiency of Chinese hydroelectric companies using a finite mixture model

This paper evaluates the operational activities of Chinese hydroelectric power companies over the period 2000-2010 using a finite mixture model that controls for unobserved heterogeneity. In so doing, a stochastic frontier latent class model, which allows for the existence of different technologies, is adopted to estimate cost frontiers. This procedure not only enables us to identify different groups among the hydro-power companies analysed, but also permits the analysis of their cost efficiency. The main result is that three groups are identified in the sample, each equipped with different technologies, suggesting that distinct business strategies need to be adapted to the characteristics of China's hydro-power companies. Some managerial implications are developed. © 2012 Elsevier B.V.

Bridging experiments, models and simulations : an integrative approach to validation in computational cardiac electrophysiology

Computational models in physiology often integrate functional and structural information from a large range of spatio-temporal scales from the ionic to the whole organ level. Their sophistication raises both expectations and scepticism concerning how computational methods can improve our understanding of living organisms and also how they can reduce, replace and refine animal experiments. A fundamental requirement to fulfil these expectations and achieve the full potential of computational physiology is a clear understanding of what models represent and how they can be validated. The present study aims at informing strategies for validation by elucidating the complex interrelations between experiments, models and simulations in cardiac electrophysiology. We describe the processes, data and knowledge involved in the construction of whole ventricular multiscale models of cardiac electrophysiology. Our analysis reveals that models, simulations, and experiments are intertwined, in an assemblage that is a system itself, namely the model-simulation-experiment (MSE) system. Validation must therefore take into account the complex interplay between models, simulations and experiments. Key points for developing strategies for validation are: 1) understanding sources of bio-variability is crucial to the comparison between simulation and experimental results; 2) robustness of techniques and tools is a pre-requisite to conducting physiological investigations using the MSE system; 3) definition and adoption of standards facilitates interoperability of experiments, models and simulations; 4) physiological validation must be understood as an iterative process that defines the specific aspects of electrophysiology the MSE system targets, and is driven by advancements in experimental and computational methods and the combination of both.

Stabilization of experimental bioretention basins during intermittent wetting and drying

Stormwater bioretention basins are subjected to spontaneous intermittent wetting and drying, unlike water treatment filter systems that are subjected to continuous feed. Drinking water filters when constructed new or after back-wash, are subjected to a phase of stabilization. Experiments show that bioretention basins are similarly impacted by intermittent wetting and drying. The common parameter monitored in the stabilisation of filters is the concentration of total solids in the outflow. Filter media in bioretention basins however, consists of a mix of particulate organic matter and fine sand. Organic carbon and solids are therefore needed to be monitored. Four Perspex bioretention filter columns of 94 mm (ID) were packed with a filter layer (800 mm), transition layer and a gravel layer and operated with synthetic stormwater in the laboratory. The filter layer contained 8% organic material by weight. A free board of 350 mm provided detention storage and head to facilitate infiltration. Synthetic stormwater was prepared by adding NH4NO3 (ammonium nitrate) and C2H5NO2 (glycine) and a mixture of kaolinite and montmorillonite clay, to tapwater. The columns were fed with synthetic stormwater with different Antecedent Dry Days (ADD) (0 – 25 day) and constant inflow concentration (2 ppm: nitrate-nitrogen, 1.5 ppm: ammonium-nitrogen, 2.5 ppm: organic-nitrogen 100 ppm: total suspended solids and 7 ppm: organic carbon) at a feed rate of 100mL.min (85.7cm/h). Samples were collected from the outflow at different time intervals between 2 – 150 min from the start of outflow and were tested for Total Suspended Solids (TSS) and Total Organic Carbon (TOC). Both TSS and TOC concentrations in the outflow were observed to be much higher than the concentration of both the parameters in the inflow during the stabilisation period indicating a phase of wash-off (first flush) which lasted for approximately 30 min for both parameters at the beginning of each storm event. The wash-off of TSS and TOC were found to be highly variable depending on the age of the filter and the number of antecedent dry days. The duration of stabilisation phase in the experiments is significant compared with many of the stormwater events. A computational analysis on total mass of each pollutant further affirmed the significance of the first flush of an event on removal of these pollutants. Therefore, the kinetics of the first flush in the stabilisation phase needs to be considered in the performance analysis of the systems.

Staging childhoods : experiments in authentic theatre making practice with children

This practice-led PhD project investigates the ways in which society positions children through a broad set of social, cultural and historical considerations and examines these within the frame of theatre making. The study proposes a model of practice that moves beyond participant empowerment toward a more dynamic understanding of the creative process that sees adults and children working together to create mainstream artistic product. It contends that this model creates conditions conducive to authentic theatre making practice with children as evidenced in the researcher’s original performance work Joy Fear and Poetry.

Social clothing experiments

Social Clothing Experiments was a large-scale outdoor installation staged for the opening of the Pacific Standard Time exhibition at the Getty Center in 2011. It was part of a ten day performance festival.Each body-pillow was made out of second-hand tie-dyed t-shirts that were patch-worked together in various formations. The public was welcomed to move, play and rest with the installation.It explores Wyman's interest in art's role in social engagement and participation.

God Bless come back : new experiments with nostalgia in Indonesian rock

This article discusses the production of an Indonesian rock past through a case study of the 1970s rock band God Bless, which has been gradually ‘coming back’ since the middle of the 2000s. In doing so, the article documents this comeback, analyses shifts in the band’s position vis-à-vis nationality, and places these shifts in the context of the industrial and aesthetic transformation of Indonesian popular music over the past decade or so. Furthermore, it considers how the range of nostalgic productions associated with the comeback might be understood not only in light of the scholarship on nostalgia, but also the political environment it inhabits.

Choosing an appropriate modelling framework for analysing multispecies co-culture cell biology experiments

In vitro cell biology assays play a crucial role in informing our understanding of the migratory, proliferative and invasive properties of many cell types in different biological contexts. While mono-culture assays involve the study of a population of cells composed of a single cell type, co-culture assays study a population of cells composed of multiple cell types (or subpopulations of cells). Such co-culture assays can provide more realistic insights into many biological processes including tissue repair, tissue regeneration and malignant spreading. Typically, system parameters, such as motility and proliferation rates, are estimated by calibrating a mathematical or computational model to the observed experimental data. However, parameter estimates can be highly sensitive to the choice of model and modelling framework. This observation motivates us to consider the fundamental question of how we can best choose a model to facilitate accurate parameter estimation for a particular assay. In this work we describe three mathematical models of mono-culture and co-culture assays that include different levels of spatial detail. We study various spatial summary statistics to explore if they can be used to distinguish between the suitability of each model over a range of parameter space. Our results for mono-culture experiments are promising, in that we suggest two spatial statistics that can be used to direct model choice. However, co-culture experiments are far more challenging: we show that these same spatial statistics which provide useful insight into mono-culture systems are insuffcient for co-culture systems. Therefore, we conclude that great care ought to be exercised when estimating the parameters of co-culture assays.

Using Bayesian Mixture Models That Combine Expert Knowledge and GIS Data to Define Ecoregions

Conservation planning and management programs typically assume relatively homogeneous ecological landscapes. Such “ecoregions” serve multiple purposes: they support assessments of competing environmental values, reveal priorities for allocating scarce resources, and guide effective on-ground actions such as the acquisition of a protected area and habitat restoration. Ecoregions have evolved from a history of organism–environment interactions, and are delineated at the scale or level of detail required to support planning. Depending on the delineation method, scale, or purpose, they have been described as provinces, zones, systems, land units, classes, facets, domains, subregions, and ecological, biological, biogeographical, or environmental regions. In each case, they are essential to the development of conservation strategies and are embedded in government policies at multiple scales.

Automatic UAV forced landing site detection using machine learning

The commercialization of aerial image processing is highly dependent on the platforms such as UAVs (Unmanned Aerial Vehicles). However, the lack of an automated UAV forced landing site detection system has been identified as one of the main impediments to allow UAV flight over populated areas in civilian airspace. This article proposes a UAV forced landing site detection system that is based on machine learning approaches including the Gaussian Mixture Model and the Support Vector Machine. A range of learning parameters are analysed including the number of Guassian mixtures, support vector kernels including linear, radial basis function Kernel (RBF) and polynormial kernel (poly), and the order of RBF kernel and polynormial kernel. Moreover, a modified footprint operator is employed during feature extraction to better describe the geometric characteristics of the local area surrounding a pixel. The performance of the presented system is compared to a baseline UAV forced landing site detection system which uses edge features and an Artificial Neural Network (ANN) region type classifier. Experiments conducted on aerial image datasets captured over typical urban environments reveal improved landing site detection can be achieved with an SVM classifier with an RBF kernel using a combination of colour and texture features. Compared to the baseline system, the proposed system provides significant improvement in term of the chance to detect a safe landing area, and the performance is more stable than the baseline in the presence of changes to the UAV altitude.