Testing alternative models for the conservation of koalas in fragmented rural-urban landscapes

Predicting the various responses of different species to changes in landscape structure is a formidable challenge to landscape ecology. Based on expert knowledge and landscape ecological theory, we develop five competing a priori models for predicting the presence/absence of the Koala (Phascolarctos cinereus) in Noosa Shire, south-east Queensland (Australia). A priori predictions were nested within three levels of ecological organization: in situ (site level) habitat (< 1 ha), patch level (100 ha) and landscape level (100-1000 ha). To test the models, Koala surveys and habitat surveys (n = 245) were conducted across the habitat mosaic. After taking into account tree species preferences, the patch and landscape context, and the neighbourhood effect of adjacent present sites, we applied logistic regression and hierarchical partitioning analyses to rank the alternative models and the explanatory variables. The strongest support was for a multilevel model, with Koala presence best predicted by the proportion of the landscape occupied by high quality habitat, the neighbourhood effect, the mean nearest neighbour distance between forest patches, the density of forest patches and the density of sealed roads. When tested against independent data (n = 105) using a receiver operator characteristic curve, the multilevel model performed moderately well. The study is consistent with recent assertions that habitat loss is the major driver of population decline, however, landscape configuration and roads have an important effect that needs to be incorporated into Koala conservation strategies.

The importance of forest area and configuration relative to local habitat factors for conserving forest mammals: A case study of koalas in Queensland, Australia

The loss and fragmentation of forest habitats by human land use are recognised as important factors influencing the decline of forest-dependent fauna. Mammal species that are dependent upon forest habitats are particularly sensitive to habitat loss and fragmentation because they have highly specific habitat requirements, and in many cases have limited ability to move through and utilise the land use matrix. We addressed this problem using a case study of the koala (Phascolarctos cinereus) surveyed in a fragmented rural-urban landscape in southeast Queensland, Australia. We applied a logistic modelling and hierarchical partitioning analysis to determine the importance of forest area and its configuration relative to site (local) and patch-level habitat variables. After taking into account spatial auto-correlation and the year of survey, we found koala occurrence increased with the area of all forest habitats, habitat patch size and the proportion of primary Eucalyptus tree species; and decreased with mean nearest neighbour distance between forest patches, the density of forest patches, and the density of sealed roads. The difference between the effect of habitat area and configuration was not as strong as theory predicts, with the configuration of remnant forest becoming increasingly important as the area of forest habitat declines. We conclude that the area of forest, its configuration across the landscape, as well as the land use matrix, are important determinants of koala occurrence, and that habitat configuration should not be overlooked in the conservation of forest-dependent mammals, such as the koala. We highlight the implications of these findings for koala conservation. (c) 2006 Elsevier Ltd. All rights reserved.

Modelling the conversion of Colombian lowland ecosystems since 1940: Drivers, patterns and rates

In biologically mega-diverse countries that are undergoing rapid human landscape transformation, it is important to understand and model the patterns of land cover change. This problem is particularly acute in Colombia, where lowland forests are being rapidly cleared for cropping and ranching. We apply a conceptual model with a nested set of a priori predictions to analyse the spatial and temporal patterns of land cover change for six 50-100 km(2) case study areas in lowland ecosystems of Colombia. Our analysis included soil fertility, a cost-distance function, and neighbourhood of forest and secondary vegetation cover as independent variables. Deforestation and forest regrowth are tested using logistic regression analysis and an information criterion approach to rank the models and predictor variables. The results show that: (a) overall the process of deforestation is better predicted by the full model containing all variables, while for regrowth the model containing only the auto-correlated neighbourhood terms is a better predictor; (b) overall consistent patterns emerge, although there are variations across regions and time; and (c) during the transformation process, both the order of importance and significance of the drivers change. Forest cover follows a consistent logistic decline pattern across regions, with introduced pastures being the major replacement land cover type. Forest stabilizes at 2-10% of the original cover, with an average patch size of 15.4 (+/- 9.2) ha. We discuss the implications of the observed patterns and rates of land cover change for conservation planning in countries with high rates of deforestation. (c) 2005 Elsevier Ltd. All rights reserved.

Interaction of psychosocial risk factors explain increased neck problems among female office workers

This study investigated the relationship between psychosocial risk factors and (1) neck symptoms and (2) neck pain and disability as measured by the neck disability index (NDI). Female office workers employed in local private and public organizations were invited to participate, with 333 completing a questionnaire. Data were collected on various risk factors including age, negative affectivity, history of previous neck trauma, physical work environment, and task demands. Sixty-one percent of the sample reported neck symptoms lasting greater than 8 days in the last 12 months. The mean NDI of the sample was 15.5 out of 100, indicating mild neck pain and disability. In a hierarchical multivariate logistic regression, low supervisor support was the only psychosocial risk factor identified with the presence of neck symptoms. Similarly, low supervisor support was the only factor associated with the score on the NDI. These associations remained after adjustment for potential confounders of age, negative affectivity, and physical risk factors. The interaction of job demands, decision authority, and supervisor support was significantly associated with the NDI in the final model and this association increased when those with previous trauma were excluded. Interestingly, and somewhat contrary to initial expectations, as job demands increased, high decision authority had an increasing effect on the NDI when supervisor support was low. Crown copyright (c) 2006 Published by Elsevier B.V. All rights reserved.

Predictors of osteoarthritis(OA) improvement in patients treated with Hylan G-F for knee OA.

Aim of study: Different criteria for treatment response were explored to identify predictors of OA improvement. Analyses were based on data from a previously reported 1-year randomized controlled trial of appropriate care with or without hylan G-F 20 in patients with knee OA. Methods: Five definitions of ‘‘patient responder’’ from baseline to month 12 were examined: at least 20% reduction in WOMAC pain score; at least 20% reduction in WOMAC pain score and at least 20% reduction in either the WOMAC stiffness or function score; OARSI responder criteria (Propositions A and B) for intra-articular drugs; and OMERACT-OARSI responder criteria (Proposition D). As an a posteriori analysis, multivariable logistic regression models for each definition of patient responder were developed using a forward selection method. The following variables were defined prior to modeling and considered in the model along with two-way interactions: age (O65 years), BMI, gender, X-ray grade (0, I, II vs III, IV), co-morbidity (1 or 2 conditions vs 3 or more), duration of OA in study knee (years), previous surgery of study knee, hylan G-F 20 injection technique, WOMAC pain, stiffness and function, and treatment group. Results: Hylan G-F 20 was a predictor of improvement for all patient responder definitions P ! 0.001; odds of improvement were 2.7 or higher for patients in the hylan G-F 20 group compared to appropriate care without hylan G-F 20. For three of the five patient responder definitions, X-ray grade was a predictor of improvement (P ! 0.10; lower X-ray grade increased the odds of improvement). For four of the five patient responder definitions, duration of OA was a predictor of improvement (P ! 0.10; shorter duration of OA increased the odds of improvement). Conclusion: Analyses showed that appropriate care with hylan G-F 20 is the dominant predictor of patient improvement. While high grade structural damage does not preclude a response, patients who are targeted early in the disease process when less structural damage has occurred, may have a greater chance of improvement.

The impact of target board recommendations in Australian takeovers

This paper examines the impact of targe board recommendations on the probability of the bid being successful in the Australian takeovers context. Specifically, we model the success rate of the bid as a binary dependent variable and target board recommendations or the board hostility as our key independent variable by using logistic regression framework. Our model also includes bid structures and conditions variables (such as initial bid premium, bid conditions, toehold, and interlocking relationship) and bid events (such as panel and bid duration) as our control variables. Overall, we find board hostility has statistically significant negative effect on the success rate of the bid and almost all control variables (except for the initial bid premium) are statistically significant with the correct sign. That is, we find toehold, the percentage of share required to make the bid becomes successful, and the unconditional bid have positive impact on the success rate of the bid, at least as predictive determinants prior to the release of any hostile recommendation. Consistent with Craswell (2004), we also find the negative relation between interlocking relationship and the success rate of the bid. Our finding supports that from target investors’ point of view, interlock is consistent with the negative story of self interest by directors. Finally, like Walking (1985), we find that the initial bid premium does not have influence on the success rate of the bid. Hence our results reinstate Walking’s bid premium puzzle in Australian context.

Guiding local regression using visualisation

Solving many scientific problems requires effective regression and/or classification models for large high-dimensional datasets. Experts from these problem domains (e.g. biologists, chemists, financial analysts) have insights into the domain which can be helpful in developing powerful models but they need a modelling framework that helps them to use these insights. Data visualisation is an effective technique for presenting data and requiring feedback from the experts. A single global regression model can rarely capture the full behavioural variability of a huge multi-dimensional dataset. Instead, local regression models, each focused on a separate area of input space, often work better since the behaviour of different areas may vary. Classical local models such as Mixture of Experts segment the input space automatically, which is not always effective and it also lacks involvement of the domain experts to guide a meaningful segmentation of the input space. In this paper we addresses this issue by allowing domain experts to interactively segment the input space using data visualisation. The segmentation output obtained is then further used to develop effective local regression models.

Potential risk factors for medication non-adherence in patients with chronic obstructive pulmonary disease (COPD)

Aims - To investigate the effect of a range of demographic and psychosocial variables on medication adherence in chronic obstructive pulmonary disease (COPD) patients managed in a secondary care setting. Methods - A total of 173 patients with a confirmed diagnosis of COPD, recruited from an outpatient clinic in Northern Ireland, participated in the study. Data collection was carried out via face-to-face interviews and through review of patients’ medical charts. Social and demographic variables, co-morbidity, self-reported drug adherence (Morisky scale), Hospital Anxiety and Depression (HAD) scale, COPD knowledge, Health Belief Model (HBM) and self-efficacy scales were determined for each patient. Results - Participants were aged 67 ± 9.7 (mean ± SD) years, 56 % female and took a mean (SD) of 8.2 ± 3.4 drugs. Low adherence with medications was present in 29.5 % of the patients. Demographic variables (gender, age, marital status, living arrangements and occupation) were not associated with adherence. A range of clinical and psychosocial variables, on the other hand, were found to be associated with medication adherence, i.e. beliefs regarding medication effectiveness, severity of COPD, smoking status, presence of co-morbid illness, depressed mood, self-efficacy, perceived susceptibility and perceived barriers within the HBM (p < 0.05). Logistic regression analysis showed that perceived ineffectiveness of medication, presence of co-morbid illness, depressed mood and perceived barriers were independently associated with medication non-adherence in the study (P < 0.05). Conclusions - Adherence in COPD patients is influenced more by patients’ perception of their health and medication effectiveness, the presence of depressed mood and co-morbid illness than by demographic factors or disease severity.

Guiding local regression using visualisation

Solving many scientific problems requires effective regression and/or classification models for large high-dimensional datasets. Experts from these problem domains (e.g. biologists, chemists, financial analysts) have insights into the domain which can be helpful in developing powerful models but they need a modelling framework that helps them to use these insights. Data visualisation is an effective technique for presenting data and requiring feedback from the experts. A single global regression model can rarely capture the full behavioural variability of a huge multi-dimensional dataset. Instead, local regression models, each focused on a separate area of input space, often work better since the behaviour of different areas may vary. Classical local models such as Mixture of Experts segment the input space automatically, which is not always effective and it also lacks involvement of the domain experts to guide a meaningful segmentation of the input space. In this paper we addresses this issue by allowing domain experts to interactively segment the input space using data visualisation. The segmentation output obtained is then further used to develop effective local regression models.

Consumer trial, continuous use, and economic benefits of a retail service innovation:the case of the personal shopping assistant

Service innovations in retailing have the potential to benefit consumers as well as retailers. This research models key factors associated with the trial and continuous use of a specific self-service technology (SST), the personal shopping assistant (PSA), and estimates retailer benefits from implementing that innovation. Based on theoretical insights from prior SST studies, diffusion of innovation literature, and the technology acceptance model (TAM), this study develops specific hypotheses and tests them on a sample of 104 actual users of the PSA and 345 nonusers who shopped at the retail store offering the PSA device. Results indicate that factors affecting initial trial are different from those affecting continuous use. More specifically, consumers' trust toward the retailer, novelty seeking, and market mavenism are positively related to trial, while technology anxiety hinders the likelihood of trying the PSA. Perceived ease of use of the device positively impacts continuous use while consumers' need for interaction in shopping environments reduces the likelihood of continuous use. Importantly, there is evidence on retailer benefits from introducing the innovation since consumers using the PSA tend to spend more during each shopping trip. However, given the high costs of technology, the payback period for recovery of investments in innovation depends largely upon continued use of the innovation by consumers. Important implications are provided for retailers considering investments in new in-store service innovations. Incorporation of technology within physical stores affords opportunities for the retailer to reduce costs, while enhancing service provided to consumers. Therefore, service innovations in retailing have the potential to benefit consumers as well as retailers. This research models key factors associated with the trial and continuous use of a specific SST in the retail context, the PSA, and estimates retailer benefits from implementing that innovation. In so doing, the study contributes to the nascent area of research on SSTs in the retail sector. Based on theoretical insights from prior SST studies, diffusion of innovation literature, and the TAM, this study develops specific hypotheses regarding the (1) antecedent effects of technological anxiety, novelty seeking, market mavenism, and trust in the retailer on trial of the service innovation; (2) the effects of ease of use, perceived waiting time, and need for interaction on continuous use of the innovation; and (3) the effect of use of innovation on consumer spending at the store. The hypotheses were tested on a sample of 104 actual users of the PSA and 345 nonusers who shopped at the retail store offering the PSA device, one of the early adopters of PSA in Germany. Data were analyzed using logistic regression (antecedents of trial), multiple regression (antecedents of continuous use), and propensity score matching (assessing retailer benefits). Results indicate that factors affecting initial trial are different from those affecting continuous use. More specifically, consumers' trust toward the retailer, novelty seeking, and market mavenism are positively related to trial, while technology anxiety hinders the likelihood of trying the PSA. Perceived ease of use of the device positively impacts continuous use, while consumers' need for interaction in shopping environments reduces the likelihood of continuous use. Importantly, there is evidence on retailer benefits from introducing the innovation since consumers using the PSA tend to spend more during each shopping trip. However, given the high costs of technology, the payback period for recovery of investments in innovation depends largely upon continued use of the innovation by consumers. Important implications are provided for retailers considering investments in new in-store service innovations. The study contributes to the literature through its (1) simultaneous examination of antecedents of trial and continuous usage of a specific SST, (2) the demonstration of economic benefits of SST introduction for the retailer, and (3) contribution to the stream of research on service innovation, as against product innovation.

AllerTOP v.2 - a server for in silico prediction of allergens

Allergy is an overreaction by the immune system to a previously encountered, ordinarily harmless substance - typically proteins - resulting in skin rash, swelling of mucous membranes, sneezing or wheezing, or other abnormal conditions. The use of modified proteins is increasingly widespread: their presence in food, commercial products, such as washing powder, and medical therapeutics and diagnostics, makes predicting and identifying potential allergens a crucial societal issue. The prediction of allergens has been explored widely using bioinformatics, with many tools being developed in the last decade; many of these are freely available online. Here, we report a set of novel models for allergen prediction utilizing amino acid E-descriptors, auto- and cross-covariance transformation, and several machine learning methods for classification, including logistic regression (LR), decision tree (DT), naïve Bayes (NB), random forest (RF), multilayer perceptron (MLP) and k nearest neighbours (kNN). The best performing method was kNN with 85.3% accuracy at 5-fold cross-validation. The resulting model has been implemented in a revised version of the AllerTOP server (http://www.ddg-pharmfac.net/AllerTOP). © Springer-Verlag 2014.

Application of MARS for the Construction of Nonparametric Models

2000 Mathematics Subject Classification: 62G08, 62P30.

Research on rework strategies for reconfigurable manufacturing system considering mission reliability

Rework strategies that involve different checking points as well as rework times can be applied into reconfigurable manufacturing system (RMS) with certain constraints, and effective rework strategy can significantly improve the mission reliability of manufacturing process. The mission reliability of process is a measurement of production ability of RMS, which serves as an integrated performance indicator of the production process under specified technical constraints, including time, cost and quality. To quantitatively characterize the mission reliability and basic reliability of RMS under different rework strategies, rework model of RMS was established based on the method of Logistic regression. Firstly, the functional relationship between capability and work load of manufacturing process was studied through statistically analyzing a large number of historical data obtained in actual machining processes. Secondly, the output, mission reliability and unit cost in different rework paths were calculated and taken as the decision variables based on different input quantities and the rework model mentioned above. Thirdly, optimal rework strategies for different input quantities were determined by calculating the weighted decision values and analyzing advantages and disadvantages of each rework strategy. At last, case application were demonstrated to prove the efficiency of the proposed method.

Local-Median Method of Forecasting for Regression Time Series under Outliers

2002 Mathematics Subject Classification: 62M20, 62-07, 62J05, 62P20.

About the Oral Health of Bulgarian Population over 20 Years Old

2000 Mathematics Subject Classification: 62P10, 62J12.