A Field Evaluation of the Time-of-Detection Method to Estimate Population Size and Density for Aural Avian Point Counts

The time-of-detection method for aural avian point counts is a new method of estimating abundance, allowing for uncertain probability of detection. The method has been specifically designed to allow for variation in singing rates of birds. It involves dividing the time interval of the point count into several subintervals and recording the detection history of the subintervals when each bird sings. The method can be viewed as generating data equivalent to closed capture–recapture information. The method is different from the distance and multiple-observer methods in that it is not required that all the birds sing during the point count. As this method is new and there is some concern as to how well individual birds can be followed, we carried out a field test of the method using simulated known populations of singing birds, using a laptop computer to send signals to audio stations distributed around a point. The system mimics actual aural avian point counts, but also allows us to know the size and spatial distribution of the populations we are sampling. Fifty 8-min point counts (broken into four 2-min intervals) using eight species of birds were simulated. Singing rate of an individual bird of a species was simulated following a Markovian process (singing bouts followed by periods of silence), which we felt was more realistic than a truly random process. The main emphasis of our paper is to compare results from species singing at (high and low) homogenous rates per interval with those singing at (high and low) heterogeneous rates. Population size was estimated accurately for the species simulated, with a high homogeneous probability of singing. Populations of simulated species with lower but homogeneous singing probabilities were somewhat underestimated. Populations of species simulated with heterogeneous singing probabilities were substantially underestimated. Underestimation was caused by both the very low detection probabilities of all distant individuals and by individuals with low singing rates also having very low detection probabilities.

Evaluation of a large-eddy model simulation of a mixed-phase altocumulus cloud using microwave radiometer, lidar and Doppler radar data

Using the Met Office large-eddy model (LEM) we simulate a mixed-phase altocumulus cloud that was observed from Chilbolton in southern England by a 94 GHz Doppler radar, a 905 nm lidar, a dual-wavelength microwave radiometer and also by four radiosondes. It is important to test and evaluate such simulations with observations, since there are significant differences between results from different cloud-resolving models for ice clouds. Simulating the Doppler radar and lidar data within the LEM allows us to compare observed and modelled quantities directly, and allows us to explore the relationships between observed and unobserved variables. For general-circulation models, which currently tend to give poor representations of mixed-phase clouds, the case shows the importance of using: (i) separate prognostic ice and liquid water, (ii) a vertical resolution that captures the thin layers of liquid water, and (iii) an accurate representation the subgrid vertical velocities that allow liquid water to form. It is shown that large-scale ascents and descents are significant for this case, and so the horizontally averaged LEM profiles are relaxed towards observed profiles to account for these. The LEM simulation then gives a reasonable. cloud, with an ice-water path approximately two thirds of that observed, with liquid water at the cloud top, as observed. However, the liquid-water cells that form in the updraughts at cloud top in the LEM have liquid-water paths (LWPs) up to half those observed, and there are too few cells, giving a mean LWP five to ten times smaller than observed. In reality, ice nucleation and fallout may deplete ice-nuclei concentrations at the cloud top, allowing more liquid water to form there, but this process is not represented in the model. Decreasing the heterogeneous nucleation rate in the LEM increased the LWP, which supports this hypothesis. The LEM captures the increase in the standard deviation in Doppler velocities (and so vertical winds) with height, but values are 1.5 to 4 times smaller than observed (although values are larger in an unforced model run, this only increases the modelled LWP by a factor of approximately two). The LEM data show that, for values larger than approximately 12 cm s(-1), the standard deviation in Doppler velocities provides an almost unbiased estimate of the standard deviation in vertical winds, but provides an overestimate for smaller values. Time-smoothing the observed Doppler velocities and modelled mass-squared-weighted fallspeeds shows that observed fallspeeds are approximately two-thirds of the modelled values. Decreasing the modelled fallspeeds to those observed increases the modelled IWC, giving an IWP 1.6 times that observed.

In vitro evaluation of fibrolytic enzymes as additives for maize (Zea mays L.) silage - II. Effects on rate of acidification, fibre degradation during ensiling and rumen fermentation

A study was carried out to determine the influence of fibrolytic enzymes derived from mesophilic or thermophilic fungal sources, added at ensiling, on time-course fermentation characteristics and in vitro rumen degradation of maize silage. The mesophilic enzyme was a commercial product derived from Trichodenna reesei (L), whereas the thermophilic enzyme was a crude extract produced from Thermoascus aurantiacus (Ta) in this laboratory. The fungus was cultured using maize cobs as a carbon source. The resulting fermentation extract was deionised to remove sugars and characterised for its protein concentration, main and side enzymic activities, optimal pH, protein molecular mass and isoelectric point. In an additional study, both enzymes were added to maize forage (333.5 g DM/kg, 70.0, 469.8, 227.1 and 307.5 g/kg DM of CP, NDF, ADF and starch, respectively) at two levels each, normalized according to xylanase activity, and ensiled in 0.5 kg capacity laboratory minisilos. Duplicate silos were opened at 2, 4, 8, 15, and 60 days after ensiling, and analysed for chemical characteristics. Silages from 60 days were bulked and in vitro gas production (GP) and organic matter degradability (OMD) profiles evaluated using the Reading Pressure Technique (RPT), in a completely randomised design. The crude enzyme extract contained mainly xylanase and endoglucanase activities, with very low levels of exoglucanase, which probably limited hydrolysis of filter paper. The extract contained three major protein bands of between 29 and 55 kDa, with mainly acidic isoelectric points. Ensiling maize with enzymes lowered (P < 0.05) the final silage pH, with this effect being observed throughout the ensiling process. All enzyme treatments reduced (P < 0.05) ADF contents. Treatments including Ta produced more gas (P < 0.05) than the controls after 24 h incubation in vitro, whereas end point gas production at 96 h was not affected. Addition of Ta increased (P < 0.01) OMD after 12 h (410 and 416 g/kg versus 373 g/kg), whereas both L and Ta increased (P < 0.05) OMD after 24 h. Addition of enzymes from mesophilic or thermophilic sources to maize forage at ensiling increased the rate of acidification of the silages and improved in vitro degradation kinetics, suggesting an improvement in the nutritive quality. (C) 2003 Elsevier B.V All rights reserved.

Uptake pathways: the potential of Bayesian belief networks to assist the management, monitoring and evaluation of development-orientated research

The effectiveness of development assistance has come under renewed scrutiny in recent years. In an era of growing economic liberalisation, research organisations are increasingly being asked to account for the use of public funds by demonstrating achievements. However, in the natural resources (NR) research field, conventional economic assessment techniques have focused on quantifying the impact achieved rather understanding the process that delivered it. As a result, they provide limited guidance for planners and researchers charged with selecting and implementing future research. In response, “pathways” or logic models have attracted increased interest in recent years as a remedy to this shortcoming. However, as commonly applied these suffer from two key limitations in their ability to incorporate risk and assess variance from plan. The paper reports the results of a case study that used a Bayesian belief network approach to address these limitations and outlines its potential value as a tool to assist the planning, monitoring and evaluation of development-orientated research.

Synthesis and evaluation of a solid supported molecular tweezer type receptor for cholesterol

A new macroporous stationary phase bearing 'tweezer' receptors that exhibit specificity for cholesterol has been constructed from rigid multifunctional vinylic monomers derived from 3,5-dibromobenzoic acid, propargyl alcohol and cholesterol. The synthesis of the novel tweezer monomer that contains two cholesterol receptor arms using palladium mediated Sonogashira methodologies and carbonate couplings is reported. The subsequent co-polymerisation of this tweezer monomer with a range of cross-linking agents via a 'pseudo' molecular imprinting approach afforded a diverse set of macroporous materials. The selectivity and efficacy of these materials for cholesterol binding was assessed using a chromatographic screening process. The optimum macroporous stationary phase material composition was subsequently used to construct monolithic solid phase extraction columns for use in the selective extraction of cholesterol from multi-component mixtures of structurally related steroids.

An analytic knowledge network process for construction entrepreneurship education

Purpose - The purpose of this paper is to provide a quantitative multicriteria decision-making approach to knowledge management in construction entrepreneurship education by means of an analytic knowledge network process (KANP) Design/methodology/approach- The KANP approach in the study integrates a standard industrial classification with the analytic network process (ANP). For the construction entrepreneurship education, a decision-making model named KANP.CEEM is built to apply the KANP method in the evaluation of teaching cases to facilitate the case method, which is widely adopted in entrepreneurship education at business schools. Findings- The study finds that there are eight clusters and 178 nodes in the KANP.CEEM model, and experimental research on the evaluation of teaching cases discloses that the KANP method is effective in conducting knowledge management to the entrepreneurship education. Research limitations/implications- As an experimental research, this paper ignores the concordance between a selected standard classification and others, which perhaps limits the usefulness of KANP.CEEM model elsewhere. Practical implications- As the KANP.CEEM model is built based on the standard classification codes and the embedded ANP, it is thus expected that the model has a wide potential in evaluating knowledge-based teaching materials for any education purpose with a background from the construction industry, and can be used by both faculty and students. Originality/value- This paper fulfils a knowledge management need and offers a practical tool for an academic starting out on the development of knowledge-based teaching cases and other teaching materials or for a student going through the case studies and other learning materials.

Predicting fault inflow in iterative software development processes: an industrial evaluation

This paper addresses the need for accurate predictions on the fault inflow, i.e. the number of faults found in the consecutive project weeks, in highly iterative processes. In such processes, in contrast to waterfall-like processes, fault repair and development of new features run almost in parallel. Given accurate predictions on fault inflow, managers could dynamically re-allocate resources between these different tasks in a more adequate way. Furthermore, managers could react with process improvements when the expected fault inflow is higher than desired. This study suggests software reliability growth models (SRGMs) for predicting fault inflow. Originally developed for traditional processes, the performance of these models in highly iterative processes is investigated. Additionally, a simple linear model is developed and compared to the SRGMs. The paper provides results from applying these models on fault data from three different industrial projects. One of the key findings of this study is that some SRGMs are applicable for predicting fault inflow in highly iterative processes. Moreover, the results show that the simple linear model represents a valid alternative to the SRGMs, as it provides reasonably accurate predictions and performs better in many cases.

Value management and post-occupancy evaluation: closing the loop

This paper considers the relationship between value management and facilities management. The findings are particularly relevant to large client organisations which procure new buildings on a regular basis. It is argued that the maximum effectiveness of value management can only be achieved if it is used in conjunction with an ongoing commitment to post-occupancy evaluation. SMART value management is seen to provide the means of ensuring that an individual building design is in alignment with the client’s strategic property needs. However, it is also necessary to recognise that an organisation’s strategic property needs will continually be in a state of change. Consequentially, economic and functional under-performance can only be avoided by a regular performance audit of existing property stock in accordance with changing requirements. Such a policy will ensure ongoing competitiveness through organisational learning. While post-occupancy evaluation represents an obvious additional service to be provided by value management consultants, it is vital that the necessary additional skills are acquired. Process management skills and social science research techniques are clearly important. However, there is also a need to improve mechanisms for data manipulation. Success can only be achieved if equal attention is given to issues of process, structure and content.

Evaluation of drug physical form during granulation, tabletting and storage

An active pharmaceutical ingredient (API) was found to dissociate from the highly crystalline hydrochloride form to the amorphous free base form, with consequent alterations to tablet properties. Here, a wet granulation manufacturing process has been investigated using in situ Fourier transform (FT)-Raman spectroscopic analyses of granules and tablets prepared with different granulating fluids and under different manufacturing conditions. Dosage form stability under a range of storage stresses was also investigated. Despite the spectral similarities between the two drug forms, low levels of API dissociation could be quantified in the tablets; the technique allowed discrimination of around 4% of the API content as the amorphous free base (i.e. less than 1% of the tablet compression weight). API dissociation was shown to be promoted by extended exposure to moisture. Aqueous granulating fluids and manufacturing delays between granulation and drying stages and storage of the tablets in open conditions at 40◦C/75% relative humidity (RH) led to dissociation. In contrast, non-aqueous granulating fluids, with no delay in processing and storage of the tablets in either sealed containers or at lower temperature/humidity prevented detectable dissociation. It is concluded that appropriate manufacturing process and storage conditions for the finished product involved minimising exposure to moisture of the API. Analysis of the drug using FT-Raman spectroscopy allowed rapid optimisation of the process whilst offering quantitative molecular information concerning the dissociation of the drug salt to the amorphous free base form.

Experience and real estate investment decision-making: a process-tracing investigation

This study investigates the impact of experience upon trained behaviours in real estate investment decision‐making. In a controlled experiment design, two groups of subjects, experts and novices, conduct an evaluation and reach a decision about two investment options. Using a process‐tracing technique, each subject’s behaviour is observed and recorded. Differences between the groups are discovered in relation to some behaviour characteristics, but experience appears not to impact all behaviours. These findings are discussed in relation to the current absence of a universal normative model of real estate investment decision‐making. In an associated component of the study, the belief that monetary compensation is needed in order to render valid results from studies such as this is tested. We find this not to be the case.

Natural caprine whey oligosaccharides separated by membrane technology and profile evaluation by capillary electrophoresis

The functional food market is growing rapidly and membrane processing offers several advantages over conventional methods for separation, fractionation and recovery of bioactive components. The aim of the present study was to select a process that could be implemented easily on an industrial scale for the isolation of natural lactose-derived oligosaccharides (OS) from caprine whey, enabling the development of functional foods for clinical and infant nutrition. The most efficient process was the combination of a pre-treatment to eliminate proteins and fat, using an ultrafiltration (UF) membrane of 25 kDa molecular weight cut off (MWCO), followed by a tighter UF membrane with 1 kDa MWCO. Circa 90% of the carbohydrates recovered in the final retentate were OS. Capillary electrophoresis was used to evaluate the OS profile in this retentate. The combined membrane-processing system is thus a promising technique for obtaining natural concentrated OS from whey. Powered

Evaluation of Earth system models using modern and palaeo-observations: the state-of-the-art

Earth system models are increasing in complexity and incorporating more processes than their predecessors, making them important tools for studying the global carbon cycle. However, their coupled behaviour has only recently been examined in any detail, and has yielded a very wide range of outcomes, with coupled climate-carbon cycle models that represent land-use change simulating total land carbon stores by 2100 that vary by as much as 600 Pg C given the same emissions scenario. This large uncertainty is associated with differences in how key processes are simulated in different models, and illustrates the necessity of determining which models are most realistic using rigorous model evaluation methodologies. Here we assess the state-of-the-art with respect to evaluation of Earth system models, with a particular emphasis on the simulation of the carbon cycle and associated biospheric processes. We examine some of the new advances and remaining uncertainties relating to (i) modern and palaeo data and (ii) metrics for evaluation, and discuss a range of strategies, such as the inclusion of pre-calibration, combined process- and system-level evaluation, and the use of emergent constraints, that can contribute towards the development of more robust evaluation schemes. An increasingly data-rich environment offers more opportunities for model evaluation, but it is also a challenge, as more knowledge about data uncertainties is required in order to determine robust evaluation methodologies that move the field of ESM evaluation from "beauty contest" toward the development of useful constraints on model behaviour.

Development and evaluation of an Earth-System model – HadGEM2

We describe here the development and evaluation of an Earth system model suitable for centennial-scale climate prediction. The principal new components added to the physical climate model are the terrestrial and ocean ecosystems and gas-phase tropospheric chemistry, along with their coupled interactions. The individual Earth system components are described briefly and the relevant interactions between the components are explained. Because the multiple interactions could lead to unstable feedbacks, we go through a careful process of model spin up to ensure that all components are stable and the interactions balanced. This spun-up configuration is evaluated against observed data for the Earth system components and is generally found to perform very satisfactorily. The reason for the evaluation phase is that the model is to be used for the core climate simulations carried out by the Met Office Hadley Centre for the Coupled Model Intercomparison Project (CMIP5), so it is essential that addition of the extra complexity does not detract substantially from its climate performance. Localised changes in some specific meteorological variables can be identified, but the impacts on the overall simulation of present day climate are slight. This model is proving valuable both for climate predictions, and for investigating the strengths of biogeochemical feedbacks.

Evaluation of biospheric components in Earth system models using modern and palaeo-observations: the state-of-the-art

Earth system models (ESMs) are increasing in complexity by incorporating more processes than their predecessors, making them potentially important tools for studying the evolution of climate and associated biogeochemical cycles. However, their coupled behaviour has only recently been examined in any detail, and has yielded a very wide range of outcomes. For example, coupled climate–carbon cycle models that represent land-use change simulate total land carbon stores at 2100 that vary by as much as 600 Pg C, given the same emissions scenario. This large uncertainty is associated with differences in how key processes are simulated in different models, and illustrates the necessity of determining which models are most realistic using rigorous methods of model evaluation. Here we assess the state-of-the-art in evaluation of ESMs, with a particular emphasis on the simulation of the carbon cycle and associated biospheric processes. We examine some of the new advances and remaining uncertainties relating to (i) modern and palaeodata and (ii) metrics for evaluation. We note that the practice of averaging results from many models is unreliable and no substitute for proper evaluation of individual models. We discuss a range of strategies, such as the inclusion of pre-calibration, combined process- and system-level evaluation, and the use of emergent constraints, that can contribute to the development of more robust evaluation schemes. An increasingly data-rich environment offers more opportunities for model evaluation, but also presents a challenge. Improved knowledge of data uncertainties is still necessary to move the field of ESM evaluation away from a "beauty contest" towards the development of useful constraints on model outcomes.

An introduction to the Chinese Evaluation Standard for the indoor thermal environment

Designing for indoor thermal environmental conditions is one of the key elements in the energy efficient building design process. This paper introduces a development of the Chinese national Evaluation Standard for indoor thermal environments (Evaluation Standard). International standards including the ASHRAE55, ISO7730, DIN EN, and CIBSE Guide-A have been reviewed and referenced for the development of the Evaluation Standard. In addition, over 28,000 subjects participated in the field study from different climate zones in China and over 500 subjects have been involved in laboratory studies. The research findings reveal that there is a need to update the Chinese thermal comfort standard based on local climates and people's habitats. This paper introduces in detail the requirements for the thermal environment for heated and cooled buildings and free-running buildings in China.