Computer-assisted sampling of acoustic data for more efficient determination of bird species richness

Bird species richness survey is one of the most intriguing ecological topics for evaluating environmental health. Here, bird species richness denotes the number of unique bird species in a particular area. Factors affecting the investigation of bird species richness include weather, observation bias, and most importantly, the prohibitive costs of conducting surveys at large spatiotemporal scales. Thanks to advances in recording techniques, these problems have been alleviated by deploying sensors for acoustic data collection. Although automated detection techniques have been introduced to identify various bird species, the innate complexity of bird vocalizations, the background noise present in the recording and the escalating volumes of acoustic data pose a challenging task on determination of bird species richness. In this paper we proposed a two-step computer-assisted sampling approach for determining bird species richness in one-day acoustic data. First, a classification model is built based on acoustic indices for filtering out minutes that contain few bird species. Then the classified bird minutes are ordered by an acoustic index and the redundant temporal minutes are removed from the ranked minute sequence. The experimental results show that our method is more efficient in directing experts for determination of bird species compared with the previous methods.

Efficient regression analysis with ranked-set sampling

This article is motivated by a lung cancer study where a regression model is involved and the response variable is too expensive to measure but the predictor variable can be measured easily with relatively negligible cost. This situation occurs quite often in medical studies, quantitative genetics, and ecological and environmental studies. In this article, by using the idea of ranked-set sampling (RSS), we develop sampling strategies that can reduce cost and increase efficiency of the regression analysis for the above-mentioned situation. The developed method is applied retrospectively to a lung cancer study. In the lung cancer study, the interest is to investigate the association between smoking status and three biomarkers: polyphenol DNA adducts, micronuclei, and sister chromatic exchanges. Optimal sampling schemes with different optimality criteria such as A-, D-, and integrated mean square error (IMSE)-optimality are considered in the application. With set size 10 in RSS, the improvement of the optimal schemes over simple random sampling (SRS) is great. For instance, by using the optimal scheme with IMSE-optimality, the IMSEs of the estimated regression functions for the three biomarkers are reduced to about half of those incurred by using SRS.

Efficient eucalypt cell wall deconstruction and conversion for sustainable lignocellulosic biofuels

In order to meet the world’s growing energy demand and reduce the impact of greenhouse gas emissions resulting from fossil fuel combustion, renewable plant-based feedstocks for biofuel production must be considered. The first-generation biofuels, derived from starches of edible feedstocks, such as corn, create competition between food and fuel resources, both for the crop itself and the land on which it is grown. As such, biofuel synthesized from non-edible plant biomass (lignocellulose) generated on marginal agricultural land will help to alleviate this competition. Eucalypts, the broadly defined taxa encompassing over 900 species of Eucalyptus, Corymbia, and Angophora are the most widely planted hardwood tree in the world, harvested mainly for timber, pulp and paper, and biomaterial products. More recently, due to their exceptional growth rate and amenability to grow under a wide range of environmental conditions, eucalypts are a leading option for the development of a sustainable lignocellulosic biofuels. However, efficient conversion of woody biomass into fermentable monomeric sugars is largely dependent on pretreatment of the cell wall, whose formation and complexity lend itself toward natural recalcitrance against its efficient deconstruction. A greater understanding of this complexity within the context of various pretreatments will allow the design of new and effective deconstruction processes for bioenergy production. In this review, we present the various pretreatment options for eucalypts, including research into understanding structure and formation of the eucalypt cell wall.

Efficient search for statistically significant dependency rules in binary data

Analyzing statistical dependencies is a fundamental problem in all empirical science. Dependencies help us understand causes and effects, create new scientific theories, and invent cures to problems. Nowadays, large amounts of data is available, but efficient computational tools for analyzing the data are missing. In this research, we develop efficient algorithms for a commonly occurring search problem - searching for the statistically most significant dependency rules in binary data. We consider dependency rules of the form X->A or X->not A, where X is a set of positive-valued attributes and A is a single attribute. Such rules describe which factors either increase or decrease the probability of the consequent A. A classical example are genetic and environmental factors, which can either cause or prevent a disease. The emphasis in this research is that the discovered dependencies should be genuine - i.e. they should also hold in future data. This is an important distinction from the traditional association rules, which - in spite of their name and a similar appearance to dependency rules - do not necessarily represent statistical dependencies at all or represent only spurious connections, which occur by chance. Therefore, the principal objective is to search for the rules with statistical significance measures. Another important objective is to search for only non-redundant rules, which express the real causes of dependence, without any occasional extra factors. The extra factors do not add any new information on the dependence, but can only blur it and make it less accurate in future data. The problem is computationally very demanding, because the number of all possible rules increases exponentially with the number of attributes. In addition, neither the statistical dependency nor the statistical significance are monotonic properties, which means that the traditional pruning techniques do not work. As a solution, we first derive the mathematical basis for pruning the search space with any well-behaving statistical significance measures. The mathematical theory is complemented by a new algorithmic invention, which enables an efficient search without any heuristic restrictions. The resulting algorithm can be used to search for both positive and negative dependencies with any commonly used statistical measures, like Fisher's exact test, the chi-squared measure, mutual information, and z scores. According to our experiments, the algorithm is well-scalable, especially with Fisher's exact test. It can easily handle even the densest data sets with 10000-20000 attributes. Still, the results are globally optimal, which is a remarkable improvement over the existing solutions. In practice, this means that the user does not have to worry whether the dependencies hold in future data or if the data still contains better, but undiscovered dependencies.

An Investigation of Environmental Conditions Experienced During the Life of High Value Wood Components and Products

Australian forest industries have a long history of export trade of a wide range of products from woodchips(for paper manufacturing), sandalwood (essential oils, carving and incense) to high value musical instruments, flooring and outdoor furniture. For the high value group, fluctuating environmental conditions brought on by changes in mperature and relative humidity, can lead to performance problems due to consequential swelling, shrinkage and/or distortion of the wood elements. A survey determined the types of value-added products exported, including species and dimensions packaging used and export markets. Data loggers were installed with shipments to monitor temperature and relative humidity conditions. These data were converted to timber equilibrium moisture content values to provide an indication of the environment that the wood elements would be acclimatising to. The results of the initial survey indicated that primary high value wood export products included guitars, flooring, decking and outdoor furniture. The destination markets were mainly located in the northern hemisphere, particularly the United States of America, China, Hong Kong, Europe including the United Kingdom), Japan, Korea and the Middle East. Other regions importing Australian-made wooden articles were south-east Asia, New Zealand and South Africa. Different timber species have differing rates of swelling and shrinkage, so the types of timber were also recorded during the survey. Results from this work determined that the major species were ash-type eucalypts from south-eastern Australia (commonly referred to in the market as Tasmanian oak), jarrah from Western Australia, spotted gum, hoop pine, white cypress, black butt, brush box and Sydney blue gum from Queensland and New South Wales. The environmental conditions data indicated that microclimates in shipping containers can fluctuate extensively during shipping. Conditions at the time of manufacturing were usually between 10 and 12% equilibrium moisture content, however conditions during shipping could range from 5 (very dry) to 20% (very humid). The packaging systems incorporated were reported to be efficient at protecting the wooden articles from damage during transit. The research highlighted the potential risk for wood components to ‘move’ in response to periods of drier or more humid conditions than those at the time of manufacturing, and the importance of engineering a packaging system that can account for the environmental conditions experienced in shipping containers. Examples of potential dimensional changes in wooden components were calculated based on published unit shrinkage data for key species and the climatic data returned from the logging equipment. The information highlighted the importance of good design to account for possible timber movement during shipping. A timber movement calculator was developed to allow designers to input component species, dimensions, site of manufacture and destination, to see validate their product design. This calculator forms part of the free interactive website www.timbers.com.au.

Economic Efficiency, Environmental Effectiveness and Political Feasibility of Energy Efficiency Rebates: The Case of the Spanish Energy Efficiency 'Renove' Plan

29 p.

Magnetic Cross-Linked Enzyme Aggregates (mCLEAs) of Candida antarctica Lipase: An Efficient and Stable Biocatalyst for Biodiesel Synthesis

Enzyme-catalyzed production of biodiesel is the object of extensive research due to the global shortage of fossil fuels and increased environmental concerns. Herein we report the preparation and main characteristics of a novel biocatalyst consisting of Cross-Linked Enzyme Aggregates (CLEAs) of Candida antarctica lipase B (CALB) which are covalently bound to magnetic nanoparticles, and tackle its use for the synthesis of biodiesel from non-edible vegetable and waste frying oils. For this purpose, insolubilized CALB was covalently cross-linked to magnetic nanoparticles of magnetite which the surface was functionalized with –NH2 groups. The resulting biocatalyst combines the relevant catalytic properties of CLEAs (as great stability and feasibility for their reutilization) and the magnetic character, and thus the final product (mCLEAs) are superparamagnetic particles of a robust catalyst which is more stable than the free enzyme, easily recoverable from the reaction medium and reusable for new catalytic cycles. We have studied the main properties of this biocatalyst and we have assessed its utility to catalyze transesterification reactions to obtain biodiesel from non-edible vegetable oils including unrefined soybean, jatropha and cameline, as well as waste frying oil. Using 1% mCLEAs (w/w of oil) conversions near 80% were routinely obtained at 30°C after 24 h of reaction, this value rising to 92% after 72 h. Moreover, the magnetic biocatalyst can be easily recovered from the reaction mixture and reused for at least ten consecutive cycles of 24 h without apparent loss of activity. The obtained results suggest that mCLEAs prepared from CALB can become a powerful biocatalyst for application at industrial scale with better performance than those currently available.

Contributions to the efficient use of general purpose coprocessors: kernel density estimation as case study

142 p.

PGPRs and nitrogen-fixing legumes: a perfect team for efficient Cd phytoremediation?

Cadmium (Cd) is a toxic, biologically non-essential and highly mobile metal that has become an increasingly important environmental hazard to both wildlife and humans. In contrast to conventional remediation technologies, phytoremediation based on legume rhizobia symbiosis has emerged as an inexpensive decontamination alternative which also revitalize contaminated soils due to the role of legumes in nitrogen cycling. In recent years, there is a growing interest in understanding symbiotic legume rhizobia relationship and its interactions with Cd. The aim of the present review is to provide a comprehensive picture of the main effects of Cd in N-2-fixing leguminous plants and the benefits of exploiting this symbiosis together with plant growth promoting rhizobacteria to boost an efficient reclamation of Cd-contaminated soils.

Is UK Planning a Barrier to Energy Efficient Heritage Retrofit: A Comparative Analysis of a Selection of London Boroughs

This paper is part of a larger PhD research project examining the apparent conflict in UK planning between energy efficiency and conservation for the retrofit of the thermal envelope of the existing building stock. Review of the literature shows that the UK will not meet its 2050 emission reduction target without substantial improvement to the energy performance of the thermal envelope of the existing building stock and that significantly, 40% of the existing stock has heritage status and may be exempted from Building Regulations. A review of UK policy and legislation shows that there are clear national priorities towards reducing emissions and addressing climate change, yet also shows a movement towards local decision making and control. This paper compares the current status of thirteen London Boroughs in respect to their position on thermal envelope retrofit for heritage and traditionally constructed buildings. Data collection is through ongoing surveys and interviews that compare statistical data, planning policies, sustainability and environmental priorities, and Officer decision-making. This paper finds that there is a lack of consistency in application of planning policy across Boroughs and suggests that this is a barrier to the up-take of energy efficient retrofit. Various recommendations are suggested at both national and local level which could help UK planning and planning officers deliver more energy efficient heritage retrofits.

Detection of hepatotoxic Microcystis strains by PCR with intact cells from both culture and environmental samples

Microcystins are small hepatotoxic peptides produced by a number of cyanobacteria. They are synthesized non-ribosomally by multifunctional enzyme complex synthetases encoded by the mcy genes. Primers deduced from mcy genes were designed to discriminate between toxic microcystin-producing strains and non-toxic strains. Thus, PCR-mediated detection of mcy genes could be a simple and efficient means to identify potentially harmful genotypes among cyanobacterial populations in bodies of water. We surveyed the distribution of the mcyB gene in different Microcystis strains isolated from Chinese bodies of water and confirmed that PCR can be reliably used to identify toxic strains. By omitting any DNA purification steps, the modified PCR protocol can greatly simplify the process. Cyanobacterial cells enriched from cultures, field samples, or even sediment samples could be used in the PCR assay. This method proved sensitive enough to detect mcyB genes in samples with less than 2,000 Microcystis cells per ml. Its accuracy, specificity and applicability were confirmed by sequencing selected DNA amplicons, as well as by HPLC, ELISA and mouse bioassay as controls for toxin production of every strain used.

Biosorption of copper (II) and lead (II) from aqueous solutions by nonliving green algae Cladophora fascicularis: Equilibrium, kinetics and environmental effects

Biosorption of Cu2+ and Pb2+ by Cladophora fascicularis was investigated as a function of initial pH, initial heavy metal concentrations, temperature and other co-existing ions. Adsorption equilibriums were well described by Langmuir and Freundlich isotherm models. The maximum adsorption capacities were 1.61 mmol/ g for Cu2+ and 0.96 mmol/ g for Pb2+ at 298K and pH 5.0. The adsorption processes were endothermic and biosorption heats calculated by the Langmuir constant b were 39.0 and 29.6 kJ/ mol for Cu2+ and Pb2+, respectively. The biosorption kinetics followed the pseudo- second order model. No significant effect on the uptake of Cu2+ and Pb2+ by co-existing cations and anions was observed, except EDTA. Desorption experiments indicated that Na(2)EDTA was an efficient desorbent for the recovery of Cu2+ and Pb2+ from biomass. The results showed that Cladophora fascicularis was an effective and economical biosorbent material for the removal and recovery of heavy metal ions from wastewater.

Efficient building management with IP-based wireless sensor network

Existing Building/Energy Management Systems (BMS/EMS) fail to convey holistic performance to the building manager. A 20% reduction in energy consumption can be achieved by efficiently operated buildings compared with current practice. However, in the majority of buildings, occupant comfort and energy consumption analysis is primarily restricted by available sensor and meter data. Installation of a continuous monitoring process can significantly improve the building systems’ performance. We present WSN-BMDS, an IP-based wireless sensor network building monitoring and diagnostic system. The main focus of WSN-BMDS is to obtain much higher degree of information about the building operation then current BMSs are able to provide. Our system integrates a heterogeneous set of wireless sensor nodes with IEEE 802.11 backbone routers and the Global Sensor Network (GSN) web server. Sensing data is stored in a database at the back office via UDP protocol and can be access over the Internet using GSN. Through this demonstration, we show that WSN-BMDS provides accurate measurements of air-temperature, air-humidity, light, and energy consumption for particular rooms in our target building. Our interactive graphical user interface provides a user-friendly environment showing live network topology, monitor network statistics, and run-time management actions on the network. We also demonstrate actuation by changing the artificial light level in one of the rooms.

Specification of optimum holistic building environmental and energy performance information to support informed decision making

Political drivers such as the Kyoto protocol, the EU Energy Performance of Buildings Directive and the Energy end use and Services Directive have been implemented in response to an identified need for a reduction in human related CO2 emissions. Buildings account for a significant portion of global CO2 emissions, approximately 25-30%, and it is widely acknowledged by industry and research organisations that they operate inefficiently. In parallel, unsatisfactory indoor environmental conditions have proven to negatively impact occupant productivity. Legislative drivers and client education are seen as the key motivating factors for an improvement in the holistic environmental and energy performance of a building. A symbiotic relationship exists between building indoor environmental conditions and building energy consumption. However traditional Building Management Systems and Energy Management Systems treat these separately. Conventional performance analysis compares building energy consumption with a previously recorded value or with the consumption of a similar building and does not recognise the fact that all buildings are unique. Therefore what is required is a new framework which incorporates performance comparison against a theoretical building specific ideal benchmark. Traditionally Energy Managers, who work at the operational level of organisations with respect to building performance, do not have access to ideal performance benchmark information and as a result cannot optimally operate buildings. This thesis systematically defines Holistic Environmental and Energy Management and specifies the Scenario Modelling Technique which in turn uses an ideal performance benchmark. The holistic technique uses quantified expressions of building performance and by doing so enables the profiled Energy Manager to visualise his actions and the downstream consequences of his actions in the context of overall building operation. The Ideal Building Framework facilitates the use of this technique by acting as a Building Life Cycle (BLC) data repository through which ideal building performance benchmarks are systematically structured and stored in parallel with actual performance data. The Ideal Building Framework utilises transformed data in the form of the Ideal Set of Performance Objectives and Metrics which are capable of defining the performance of any building at any stage of the BLC. It is proposed that the union of Scenario Models for an individual building would result in a building specific Combination of Performance Metrics which would in turn be stored in the BLC data repository. The Ideal Data Set underpins the Ideal Set of Performance Objectives and Metrics and is the set of measurements required to monitor the performance of the Ideal Building. A Model View describes the unique building specific data relevant to a particular project stakeholder. The energy management data and information exchange requirements that underlie a Model View implementation are detailed and incorporate traditional and proposed energy management. This thesis also specifies the Model View Methodology which complements the Ideal Building Framework. The developed Model View and Rule Set methodology process utilises stakeholder specific rule sets to define stakeholder pertinent environmental and energy performance data. This generic process further enables each stakeholder to define the resolution of data desired. For example, basic, intermediate or detailed. The Model View methodology is applicable for all project stakeholders, each requiring its own customised rule set. Two rule sets are defined in detail, the Energy Manager rule set and the LEED Accreditor rule set. This particular measurement generation process accompanied by defined View would filter and expedite data access for all stakeholders involved in building performance. Information presentation is critical for effective use of the data provided by the Ideal Building Framework and the Energy Management View definition. The specifications for a customised Information Delivery Tool account for the established profile of Energy Managers and best practice user interface design. Components of the developed tool could also be used by Facility Managers working at the tactical and strategic levels of organisations. Informed decision making is made possible through specified decision assistance processes which incorporate the Scenario Modelling and Benchmarking techniques, the Ideal Building Framework, the Energy Manager Model View, the Information Delivery Tool and the established profile of Energy Managers. The Model View and Rule Set Methodology is effectively demonstrated on an appropriate mixed use existing ‘green’ building, the Environmental Research Institute at University College Cork, using the Energy Management and LEED rule sets. Informed Decision Making is also demonstrated using a prototype scenario for the demonstration building.

Environmental context explains Lévy and Brownian movement patterns of marine predators

An optimal search theory, the so-called Levy-flight foraging hypothesis(1), predicts that predators should adopt search strategies known as Levy flights where prey is sparse and distributed unpredictably, but that Brownian movement is sufficiently efficient for locating abundant prey(2-4). Empirical studies have generated controversy because the accuracy of statistical methods that have been used to identify Levy behaviour has recently been questioned(5,6). Consequently, whether foragers exhibit Levy flights in the wild remains unclear. Crucially, moreover, it has not been tested whether observed movement patterns across natural landscapes having different expected resource distributions conform to the theory's central predictions. Here we use maximum-likelihood methods to test for Levy patterns in relation to environmental gradients in the largest animal movement data set assembled for this purpose. Strong support was found for Levy search patterns across 14 species of open-ocean predatory fish (sharks, tuna, billfish and ocean sunfish), with some individuals switching between Levy and Brownian movement as they traversed different habitat types. We tested the spatial occurrence of these two principal patterns and found Levy behaviour to be associated with less productive waters (sparser prey) and Brownian movements to be associated with productive shelf or convergence-front habitats (abundant prey). These results are consistent with the Levy-flight foraging hypothesis(1,7), supporting the contention(8,9) that organism search strategies naturally evolved in such a way that they exploit optimal Levy patterns.