Causes Problems (cause + problem)

Distribution by Scientific Domains
Mathematics and Statistics50%

Selected Abstracts

Freshwater crayfish farming technology in the 1990s: a European and global perspective

FISH AND FISHERIES, Issue 4 2000
H.E.G. Ackefors
This paper aims to describe the state of crayfish farming technology in the USA, Australia and Europe, and to discuss some of the prerequisites for this industry. Data from Europe are partly based on replies from a questionnaire sent out to scientists in all European countries. For other parts of the world, the crayfish literature has been reviewed and data from the August 2000 meeting of the International Association of Astacology are also included. Issues addressed in this review are cultivated species, production and productivity figures, production technique with regard to enclosures, reproduction and feed items, disease problems, predators, pond vegetation and water quality. Fewer than a dozen crayfish species are cultivated. The most attractive ones for culture and stocking in natural waters have been transferred to more than one continent. Pond rearing techniques predominate in all countries, and the technology required to achieve the spawning and rearing of juveniles is relatively simple. Pieces of fish, carrots and potatoes are frequent supplementary feed items; plants, cereals, pieces of meat, zooplankton and pellets are also common. Diseases are not usually a major concern, except in Europe where the American plague fungus, Aphanomyces astaci, has eradicated many European crayfish populations. Predators identified as common include insects and amphibians, as well as fishes, birds and mammals. Many water macrophytes are common in crayfish farms. These may either serve a useful function or cause problems for the crayfish farmer. Water temperature is the crucial factor for crayfish production. Water parameters such as pH and certain inorganic ion concentrations may also be of concern. Acidic waters that occur in some areas are generally detrimental to crayfish. The total yield from crayfish production from farming and fishery is in the order of 120 000,150 000 tonnes, more than four times the quantity given by FAO statistics. The largest crayfish producer is the Peoples' Republic of China, followed by the USA (70 000 and 50 000 tonnes in 1999, respectively). Of the quantity produced in the USA in 1999, about 35 000 tonnes was farmed. The yield in Europe was about 4500 tonnes in 1994, and of this quantity only 160 tonnes came from aquaculture. There are no official statistics for crayfish fishery production in Australia, but about 400 tonnes came from aquaculture in 1999. [source]

What causes problems in Alzheimer's disease: attributions by caregivers.

INTERNATIONAL JOURNAL OF GERIATRIC PSYCHIATRY, Issue 6 2004
A qualitative study
Abstract Objective To gain insight into caregivers' understanding of the causes of behaviours they find problematic in people with Alzheimer's disease in order to inform the development of educational strategies. Methods A qualitative, semi-structured interview was used. Participants were 205 caregivers for a person with Alzheimer's disease, all of whom were aware of the diagnosis and who had been recruited as part of a larger longitudinal study. Participants were from inner-city and suburban London/semi-rural Essex. The main outcome measures were caregivers' understanding of: the cause of problematic behaviour; the ability of the person with dementia to control this behaviour; the prognosis of the illness. Results Most carers attribute the cognitive, behavioural and psychological symptoms of dementia to causes other than dementia; many believe that the person with dementia has control over their behaviour and substantial numbers believe the person with dementia will return to normal. Conclusions This study suggests that providing facts about the illness to caregivers is not enough, as caregivers may not understand that the symptoms they observe are related to the diagnosis. Education by clinicians should focus on the understanding of caregivers and in particular explore the caregivers' attributions of the symptoms which are present in the person for whom they care. Copyright © 2004 John Wiley & Sons, Ltd. [source]

Fixed rank kriging for very large spatial data sets

JOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 1 2008
Noel Cressie
Summary., Spatial statistics for very large spatial data sets is challenging. The size of the data set, n, causes problems in computing optimal spatial predictors such as kriging, since its computational cost is of order . In addition, a large data set is often defined on a large spatial domain, so the spatial process of interest typically exhibits non-stationary behaviour over that domain. A flexible family of non-stationary covariance functions is defined by using a set of basis functions that is fixed in number, which leads to a spatial prediction method that we call fixed rank kriging. Specifically, fixed rank kriging is kriging within this class of non-stationary covariance functions. It relies on computational simplifications when n is very large, for obtaining the spatial best linear unbiased predictor and its mean-squared prediction error for a hidden spatial process. A method based on minimizing a weighted Frobenius norm yields best estimators of the covariance function parameters, which are then substituted into the fixed rank kriging equations. The new methodology is applied to a very large data set of total column ozone data, observed over the entire globe, where n is of the order of hundreds of thousands. [source]

Testing Random Effects in the Linear Mixed Model Using Approximate Bayes Factors

BIOMETRICS, Issue 2 2009
Benjamin R. Saville
Summary Deciding which predictor effects may vary across subjects is a difficult issue. Standard model selection criteria and test procedures are often inappropriate for comparing models with different numbers of random effects due to constraints on the parameter space of the variance components. Testing on the boundary of the parameter space changes the asymptotic distribution of some classical test statistics and causes problems in approximating Bayes factors. We propose a simple approach for testing random effects in the linear mixed model using Bayes factors. We scale each random effect to the residual variance and introduce a parameter that controls the relative contribution of each random effect free of the scale of the data. We integrate out the random effects and the variance components using closed-form solutions. The resulting integrals needed to calculate the Bayes factor are low-dimensional integrals lacking variance components and can be efficiently approximated with Laplace's method. We propose a default prior distribution on the parameter controlling the contribution of each random effect and conduct simulations to show that our method has good properties for model selection problems. Finally, we illustrate our methods on data from a clinical trial of patients with bipolar disorder and on data from an environmental study of water disinfection by-products and male reproductive outcomes. [source]