When do conservation planning methods deliver? Quantifying the consequences of uncertainty

The rapid global loss of biodiversity has led to a proliferation of systematic conservation planning methods. In spite of their utility and mathematical sophistication, these methods only provide approximate solutions to real-world problems where there is uncertainty and temporal change. The consequences of errors in these solutions are seldom characterized or addressed. We propose a conceptual structure for exploring the consequences of input uncertainty and oversimpli?ed approximations to real-world processes for any conservation planning tool or strategy. We then present a computational framework based on this structure to quantitatively model species representation and persistence outcomes across a range of uncertainties. These include factors such as land costs, landscape structure, species composition and distribution, and temporal changes in habitat. We demonstrate the utility of the framework using several reserve selection methods including simple rules of thumb and more sophisticated tools such as Marxan and Zonation. We present new results showing how outcomes can be strongly affected by variation in problem characteristics that are seldom compared across multiple studies. These characteristics include number of species prioritized, distribution of species richness and rarity, and uncertainties in the amount and quality of habitat patches. We also demonstrate how the framework allows comparisons between conservation planning strategies and their response to error under a range of conditions. Using the approach presented here will improve conservation outcomes and resource allocation by making it easier to predict and quantify the consequences of many different uncertainties and assumptions simultaneously. Our results show that without more rigorously generalizable results, it is very dif?cult to predict the amount of error in any conservation plan. These results imply the need for standard practice to include evaluating the effects of multiple real-world complications on the behavior of any conservation planning method.

A study of load support and other criteria appropriate to the selection of industrial conveyor belts

A study of conveying practice demonstrates that belt conveyors provide a versatile and. much-used method of transporting bulk materials, but a review of belting manufacturers' design procedures shows that belt design and selection rules are often based on experience with all-cotton belts no longer in common use, and are net completely relevant to modern synthetic constructions. In particular, provision of the property "load support", which was not critical with cotton belts, is shown to determine the outcome of most belt selection exercises and lead to gross over specification of other design properties in many cases. The results of an original experimental investigation into this property, carried out to determine the belt and conveyor parameters that affect it, how the major role that belt stiffness plays in its provision; the basis for a belt stiffness test relevant to service conditions is given. A proposal for a more rational method of specifying load support data results from the work, but correlation of the test results with service performance is necessary before the absolute toad support capability required from a belt for given working conditions can be quantified. A study to attain this correlation is the major proposal for future work resulting from the present investigation, but a full review of the literature on conveyor design and a study of present practice within the belting industry demonstrate other, less critical, factors that could profitably be investigated. It is suggested that the most suitable method of studying these would be a rational data collection system to provide information on various facets of belt service behaviour; a basis for such a system is proposed. In addition to the work above, proposals for simplifying the present belt selection methods are made and a strain transducer suitable for use in future experimental investigations is developed.

A survey of UK selection practices across different organization sizes and industry sectors

This paper presents results of a study examining the methods used to select employees in 579 UK organizations representing a range of different organization sizes and industry sectors. Overall, a smaller proportion of organizations in this sample reported using formalized methods (e.g., assessment centres) than informal methods (e.g., unstructured interviews). The curriculum vitae (CVs) was the most commonly used selection method, followed by the traditional triad of application form, interviews, and references. Findings also indicated that the use of different selection methods was similar in both large organizations and small-to-medium-sized enterprises. Differences were found across industry sector with public and voluntary sectors being more likely to use formalized techniques (e.g., application forms rather than CVs and structured rather than unstructured interviews). The results are discussed in relation to their implications, both in terms of practice and future research.

Higher-order co-occurrences for exploratory point pattern analysis and decision tree clustering on spatial data

Analyzing geographical patterns by collocating events, objects or their attributes has a long history in surveillance and monitoring, and is particularly applied in environmental contexts, such as ecology or epidemiology. The identification of patterns or structures at some scales can be addressed using spatial statistics, particularly marked point processes methodologies. Classification and regression trees are also related to this goal of finding "patterns" by deducing the hierarchy of influence of variables on a dependent outcome. Such variable selection methods have been applied to spatial data, but, often without explicitly acknowledging the spatial dependence. Many methods routinely used in exploratory point pattern analysis are2nd-order statistics, used in a univariate context, though there is also a wide literature on modelling methods for multivariate point pattern processes. This paper proposes an exploratory approach for multivariate spatial data using higher-order statistics built from co-occurrences of events or marks given by the point processes. A spatial entropy measure, derived from these multinomial distributions of co-occurrences at a given order, constitutes the basis of the proposed exploratory methods. © 2010 Elsevier Ltd.

Higher-order co-occurrences for exploratory point pattern analysis and decision tree clustering on spatial data

Analyzing geographical patterns by collocating events, objects or their attributes has a long history in surveillance and monitoring, and is particularly applied in environmental contexts, such as ecology or epidemiology. The identification of patterns or structures at some scales can be addressed using spatial statistics, particularly marked point processes methodologies. Classification and regression trees are also related to this goal of finding "patterns" by deducing the hierarchy of influence of variables on a dependent outcome. Such variable selection methods have been applied to spatial data, but, often without explicitly acknowledging the spatial dependence. Many methods routinely used in exploratory point pattern analysis are2nd-order statistics, used in a univariate context, though there is also a wide literature on modelling methods for multivariate point pattern processes. This paper proposes an exploratory approach for multivariate spatial data using higher-order statistics built from co-occurrences of events or marks given by the point processes. A spatial entropy measure, derived from these multinomial distributions of co-occurrences at a given order, constitutes the basis of the proposed exploratory methods. © 2010 Elsevier Ltd.

Self-adaptive probabilistic roadmap generation for intelligent virtual agents

Agents inhabiting large scale environments are faced with the problem of generating maps by which they can navigate. One solution to this problem is to use probabilistic roadmaps which rely on selecting and connecting a set of points that describe the interconnectivity of free space. However, the time required to generate these maps can be prohibitive, and agents do not typically know the environment in advance. In this paper we show that the optimal combination of different point selection methods used to create the map is dependent on the environment, no point selection method dominates. This motivates a novel self-adaptive approach for an agent to combine several point selection methods. The success rate of our approach is comparable to the state of the art and the generation cost is substantially reduced. Self-adaptation therefore enables a more efficient use of the agent's resources. Results are presented for both a set of archetypal scenarios and large scale virtual environments based in Second Life, representing real locations in London.

Improved methods for the recruitment and selection of operatives

DUE TO COPYRIGHT RESTRICTIONS ONLY AVAILABLE FOR CONSULTATION AT ASTON UNIVERSITY LIBRARY AND INFORMATION SERVICES WITH PRIOR ARRANGEMENT

Comparing methods to separate treatment from self-selection effects in an online banking setting

The literature discusses several methods to control for self-selection effects but provides little guidance on which method to use in a setting with a limited number of variables. The authors theoretically compare and empirically assess the performance of different matching methods and instrumental variable and control function methods in this type of setting by investigating the effect of online banking on product usage. Hybrid matching in combination with the Gaussian kernel algorithm outperforms the other methods with respect to predictive validity. The empirical finding of large self-selection effects indicates the importance of controlling for these effects when assessing the effectiveness of marketing activities.

The dynamics of a genetic algorithm under stabilizing selection

A formalism recently introduced by Prugel-Bennett and Shapiro uses the methods of statistical mechanics to model the dynamics of genetic algorithms. To be of more general interest than the test cases they consider. In this paper, the technique is applied to the subset sum problem, which is a combinatorial optimization problem with a strongly non-linear energy (fitness) function and many local minima under single spin flip dynamics. It is a problem which exhibits an interesting dynamics, reminiscent of stabilizing selection in population biology. The dynamics are solved under certain simplifying assumptions and are reduced to a set of difference equations for a small number of relevant quantities. The quantities used are the population's cumulants, which describe its shape, and the mean correlation within the population, which measures the microscopic similarity of population members. Including the mean correlation allows a better description of the population than the cumulants alone would provide and represents a new and important extension of the technique. The formalism includes finite population effects and describes problems of realistic size. The theory is shown to agree closely to simulations of a real genetic algorithm and the mean best energy is accurately predicted.

Expert systems for vendor selection

The survival of organisations, especially SMEs, depends, to the greatest extent, on those who supply them with the required material input. This is because if the supplier fails to deliver the right materials at the right time and place, and at the right price, then the recipient organisation is bound to fail in its obligations to satisfy the needs of its customers, and to stay in business. Hence, the task of choosing a supplier(s) from a list of vendors, that an organisation will trust with its very existence, is not an easy one. This project investigated how purchasing personnel in organisations solve the problem of vendor selection. The investigation went further to ascertain whether an Expert Systems model could be developed and used as a plausible solution to the problem. An extensive literature review indicated that very scanty research has been conducted in the area of Expert Systems for Vendor Selection, whereas many research theories in expert systems and in purchasing and supply management chain, respectively, had been reported. A survey questionnaire was designed and circulated to people in the industries who actually perform the vendor selection tasks. Analysis of the collected data confirmed the various factors which are considered during the selection process, and established the order in which those factors are ranked. Five of the factors, namely, Production Methods Used, Vendors Financial Background, Manufacturing Capacity, Size of Vendor Organisations, and Suppliers Position in the Industry; appeared to have similar patterns in the way organisations ranked them. These patterns suggested that the bigger the organisation, the more importantly they regarded the above factors. Further investigations revealed that respondents agreed that the most important factors were: Product Quality, Product Price and Delivery Date. The most apparent pattern was observed for the Vendors Financial Background. This generated curiosity which led to the design and development of a prototype expert system for assessing the financial profile of a potential supplier(s). This prototype was called ESfNS. It determines whether a prospective supplier(s) has good financial background or not. ESNS was tested by the potential users who then confirmed that expert systems have great prospects and commercial viability in the domain for solving vendor selection problems.

Computer aided selection of candidate vaccine antigens

Immunoinformatics is an emergent branch of informatics science that long ago pullulated from the tree of knowledge that is bioinformatics. It is a discipline which applies informatic techniques to problems of the immune system. To a great extent, immunoinformatics is typified by epitope prediction methods. It has found disappointingly limited use in the design and discovery of new vaccines, which is an area where proper computational support is generally lacking. Most extant vaccines are not based around isolated epitopes but rather correspond to chemically-treated or attenuated whole pathogens or correspond to individual proteins extract from whole pathogens or correspond to complex carbohydrate. In this chapter we attempt to review what progress there has been in an as-yet-underexplored area of immunoinformatics: the computational discovery of whole protein antigens. The effective development of antigen prediction methods would significantly reduce the laboratory resource required to identify pathogenic proteins as candidate subunit vaccines. We begin our review by placing antigen prediction firmly into context, exploring the role of reverse vaccinology in the design and discovery of vaccines. We also highlight several competing yet ultimately complementary methodological approaches: sub-cellular location prediction, identifying antigens using sequence similarity, and the use of sophisticated statistical approaches for predicting the probability of antigen characteristics. We end by exploring how a systems immunomics approach to the prediction of immunogenicity would prove helpful in the prediction of antigens.

Integrated project evaluation and selection using multiple-attribute decision-making technique

The evaluation and selection of industrial projects before investment decision is customarily done using marketing, technical and financial information. Subsequently, environmental impact assessment and social impact assessment are carried out mainly to satisfy the statutory agencies. Because of stricter environment regulations in developed and developing countries, quite often impact assessment suggests alternate sites, technologies, designs, and implementation methods as mitigating measures. This causes considerable delay to complete project feasibility analysis and selection as complete analysis requires to be taken up again and again till the statutory regulatory authority approves the project. Moreover, project analysis through above process often results sub-optimal project as financial analysis may eliminate better options, as more environment friendly alternative will always be cost intensive. In this circumstance, this study proposes a decision support system, which analyses projects with respect to market, technicalities, and social and environmental impact in an integrated framework using analytic hierarchy process, a multiple-attribute decision-making technique. This not only reduces duration of project evaluation and selection, but also helps select optimal project for the organization for sustainable development. The entire methodology has been applied to a cross-country oil pipeline project in India and its effectiveness has been demonstrated. © 2005 Elsevier B.V. All rights reserved.

Decision support system for project selection

The evaluation and selection of industrial projects before investment decision is customarily done using marketing, technical, and financial information. Subsequently, environmental impact assessment and social impact assessment are carried out mainly to satisfy the statutory agencies. Because of stricter environment regulations in developed and developing countries, quite often impact assessment suggests alternate sites, technologies, designs, and implementation methods as mitigating measures. This causes considerable delay to complete project feasibility analysis and selection as complete analysis requires to be taken up again and again until the statutory regulatory authority approves the project. Moreover, project analysis through the above process often results in suboptimal projects as financial analysis may eliminate better options as more environment friendly alternative will always be cost intensive. In this circumstance, this study proposes a decision support system which analyses projects with respect to market, technicalities, and social and environmental impact in an integrated framework using analytic hierarchy process, a multiple attribute decision-making technique. This not only reduces duration of project evaluation and selection, but also helps select an optimal project for the organization for sustainable development. The entire methodology has been applied to a cross-country oil pipeline project in India and its effectiveness has been demonstrated. © 2008, IGI Global.

Writing and characterisation methods for UV-photosensitivity based devices and their applications

The underlying work to this thesis focused on the exploitation and investigation of photosensitivity mechanisms in optical fibres and planar waveguides for the fabrication of advanced integrated optical devices for telecoms and sensing applications. One major scope is the improvement of grating fabrication specifications by introducing new writing techniques and the use of advanced characterisation methods for grating testing. For the first time the polarisation control method for advanced grating fabrication has successfully been converted to apodised planar waveguide fabrication and the development of a holographic method for the inscription of chirped gratings at arbitrary wavelength is presented. The latter resulted in the fabrication of gratings for pulse-width suppression and wavelength selection in diode lasers. In co-operation with research partners a number of samples were tested using optical frequency domain and optical low coherence reflectometry for a better insight into the limitations of grating writing techniques. Using a variety of different fabrication methods, custom apodised and chirped fibre Bragg gratings were written for the use as filter elements for multiplexer-demultiplexer devices, as well as for short pulse generation and wavelength selection in telecommunication transmission systems. Long period grating based devices in standard, speciality and tapered fibres are presented, showing great potential for multi-parameter sensing. One particular scope is the development of vectorial curvature and refractive index sensors with potential for medical, chemical and biological sensing. In addition the design of an optically tunable Mach-Zehnder based multiwavelength filter is introduced. The discovery of a Type IA grating type through overexposure of hydrogen loaded standard and Boron-Germanium co-doped fibres strengthened the assumption of UV-photosensitivity being a highly non-linear process. Gratings of this type show a significantly lower thermal sensitivity compared to standard gratings, which makes them useful for sensing applications. An Oxford Lasers copper-vapour laser operating at 255 nm in pulsed mode was used for their inscription, in contrast to previous work using CW-Argon-Ion lasers and contributing to differences in the processes of the photorefractive index change

Fair testing in employment selection

This project represents the collaboration of Charta Mede Ltd and the Interdisciplinary Higher Degrees Scheme at the University of Aston. The aim of the project was to monitor the effects of the Civil Service's Executive Officer Qualifying Test Battery on minority group applicants. Prior to monitoring the EO Test Battery, however, an ethnic classification had to be developed which was reliable, acceptable to respondents and appropriate for monitoring. Three pilot studies were conducted to examine these issues, during which different classifications and different ways of asking the question were trialled. The results indicated that by providing more precise instructions as to the meanings of categories, it was possible to obtain classifications which were acceptable and reliable. However, there were also certain terms and expressions which should be avoided such as those referring to colour and anthropological racial groups. Two classifications were used in the Executive Officer Study - one derived from an Office of Population Censuses and Surveys classification and one developed for this project - the MultiCultural British Classification. The results indicated that some minority groups (Asians, West Indians and Africans in particular) pass the tests in significantly lower proportions than the majority group and also score significantly less well on the tests. Factors which were significantly related to pass/fail and test scores included educational qualifications and age on entering the UK (the latter being negatively correlated). Using variables in this study, however, it was only possible to account for 5% of the variance in pass/fail rates and 11% of the variance in test scores. Analyses of covariance carried out indicated that the differences in test scores still remained even though the effects of significantly correlated variables were removed. Although indirect discrimination could not be inferred from the data, further research into differential validity and fairer methods of select ion is needed.