Performance of adaptive cluster sampling estimators in a multivariate setting

Adaptive cluster sampling has been proven to perform well in a univariate setting, but it may not perform well when there are several parameters of interest. The efficiency of adaptive sampling when there are several variables of interest depends on the relationship of the variables with one another.     

Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters

Two artificial neural networks (ANNs), unsupervised and supervised learning algorithms, were applied to suggest practical approaches for the analysis of ecological data. Four major aquatic insect orders (Ephemeroptera, Plecoptera, Trichoptera, and Coleoptera, i.e. EPTC), and four environmental variables (elevation, stream order, distance from the source, and water temperature) were used to implement the models. The data were collected and measured at 155 sampling sites on streams of the Adour–Garonne drainage basin (South-western France). The modelling procedure was carried out following two steps. First, a self-organizing map (SOM), an unsupervised ANN, was applied to classify sampling sites using EPTC richness. Second, a backpropagation algorithm (BP), a supervised ANN, was applied to predict EPTC richness using a set of four environmental variables. The trained SOM classified sampling sites according to a gradient of EPTC richness, and the groups obtained corresponded to geographic regions of the drainage basin and characteristics of their environmental variables. The SOM showed its convenience to analyze relationships among sampling sites, biological attributes, and environmental variables. After accounting for the relationships in data sets, the BP used to predict the EPTC richness with a set of four environmental variables showed a high accuracy (r=0.91 and r=0.61 for training and test data sets respectively). The prediction of EPTC richness is thus a valuable tool to assess disturbances in given areas: by knowing what the EPTC richness should be, we can determine the degree to which disturbances have altered it. The results suggested that methodologies successively using two different neural networks are helpful to understand ecological data through ordination first, and then to predict target variables.     

INCREASING REGULATORS' CONFIDENCE IN POINT-NONPOINT POLLUTANT TRADING SCHEMES1

ABSTRACT: One of the principal stumbling blocks to regulatory agencies' adopting pollutant trading schemes is the complex of uncertainties surrounding any change in institutions. This is especially true if nonpoint pollution sources are to be involved along with point sources. Regulators are understandably reluctant to switch from tried-and-true point source permit systems, even if trading schemes can be shown (on paper, at least) to result in lower public expenditures. We propose a set of practical criteria for point-nonpoint pollutant trading systems that promise to increase regulators' confidence that the new system will be equally effective in controlling pollution and at the same time more likely to capture efficiencies in pollution reduction practices.     

Incentive compensation as an injurious condition of work: A study of labelling

A recursive causal model is presented in which individual and work factors are hypothesized to be predictive of workers' labelling an incentive compensation program as potentially injurious to their well-being. The individual factors include the dispositional variable, negative affectivity, and financial needs and resources. The work factors consist of prior incentive compensation, experienced job insecurity, and experienced control. The model was tested using questionnaire responses of 165 managers and professionals. While it was found that the overall model did not fit the data that well, negative affectivity was found to be positively associated and experienced control negatively associated with the incentive compensation program being labelled negatively. The somewhat surprising finding that financial needs and resources were not found to be significantly associated with labelling is discussed. The importance of disposition and experienced control for the labelling of potentially threatening or harmful job conditions as well as some hypothesized antecedents of control also are discussed.     

Die Organisation der chemischen Sammelliteratur

The Science of Nature -     

Sensitized photooxygenation of mononitrophenols in micellar media

The singlet molecular oxygen‐mediated photooxidation of simple nitrophenols was studied in micellar (CTAB) alkaline solutions.

A different behaviour for the values of the overall rate constant (K_t ) and the reactive rate constant (K_t ) in the interaction nitrophenol‐singlet molecular oxygen was observed as a function of surfactant concentration. The former decreases as [CTAB] increases, from values of 1–3 × 10⁹ M^–1 s^–1 in the absence of surfactant to values at least one order of magnitude lower at [CTAB] ～0.1 M. Parallel, k_t remains practically unaffected, in values of the order of 10⁶ — 10⁷ M^–l s^–1, depending on the particular nitrophenol, within the same [CTAB] range. The quantum yield of nitrophenols photooxidation reaches a maximum at [CTAB] ～0.1 M.

The results are discussed in terms of a combination of causes, the polarity of the nitrophenolic microenvironment being the driving effect. The implication of the results on environmental decontamination are also analyzed.