Urbanization impacts the taxonomic and functional structure of aquatic macroinvertebrate communities in a small Neotropical city

Due to habitat fragmentation, resource disruption and pollution, urbanization is one of the most destructive forms of anthropization affecting ecosystems worldwide. Generally, human-mediated perturbations dramatically alter species diversity in urban sites compared to the surroundings, thus influencing the functioning of the entire ecosystem. We investigated the taxonomic and functional diversity patterns of the aquatic macroinvertebrate communities in tank bromeliads by comparing those found in a small Neotropical city with those from an adjacent rural site. Changes in the quality of detrital inputs in relation to lower tree diversity and the presence of synanthropic species are likely important driving forces behind the observed structural changes in the urban site. Leaf-litter processors (i.e., shredders, scrapers) were positively affected in the urban site, while filter-feeders that process smaller particles produced by the activity of the shredders were negatively affected. Because we cannot ascertain whether the decline in filter-feeders is related to food web-mediated effects or to competitive exclusion (Aedes aegypti mosquitoes were present in urban bromeliads only), further studies are necessary to account for the effects of intra-guild competition or inter-guild facilitation.     

Palm Distributions for Log Gaussian Cox Processes

This paper establishes a remarkable result regarding Palm distributions for a log Gaussian Cox process: the reduced Palm distribution for a log Gaussian Cox process is itself a log Gaussian Cox process that only differs from the original log Gaussian Cox process in the intensity function. This new result is used to study functional summaries for log Gaussian Cox processes.     

A new generalized two-sided class of distributions with an emphasis on two-sided generalized normal distribution

The ordinary-G class of distributions is defined to have the cumulative distribution function (cdf) as the value of the cdf of the ordinary distribution F whose range is the unit interval at G, that is, F(G), and it generalizes the ordinary distribution. In this work, we consider the standard two-sided power distribution to define other classes like the beta-G and the Kumaraswamy-G classes. We extend the idea of two-sidedness to other ordinary distributions like normal. After studying the basic properties of the new class in general setting, we consider the two-sided generalized normal distribution with maximum likelihood estimation procedure.     

The cost of children: May the collective approach to household behavior help?

The collective approach to household consumption behavior tries to infer from variables supposed to affect the general bargaining position of household members information on the allocation of consumptions goods and tasks among them. This paper investigates the extension of previous work to the case where children may be considered as a public consumption good by the two adult members of a household. The main question being asked is whether it is possible to retrieve from the aggregate consumption behaviour of the household and the relative earnings of the parents information on the allocation of goods between them and children. This alternative approach to the estimation of the ‘cost of children’ is contrasted with the conventional approach based on a ‘unitary’ representation of and demographic separability assumptions on household consumption behaviour. Received: 29 August 1997/Accepted: 26 November 1998     

Low wages and high unemployment rates: The role of social interactions in hiring discrimination

The purpose of this paper is to explain why low-wage workers with identical qualifications to higher-wage workers are more exposed to unemployment. Each worker is considered to belong to a social group (defined according to his/her gender, age, and nationality). We assume that workers experience both productive interdependencies and social interactions within the firm. Also inter- and intra-group interactions determine worker productivity, and frictions on the labor market limit the hiring of the most productive workers. Consequently, externalities acting both within the firm and in the labor market can lead to a higher rate of unemployment for low-wage workers.     

Annualized hours as a capacity planning tool in make-to-order or assemble-to-order environment: An agricultural implements company case

Capacity planning is one of the most important elements for an efficient production planning and control system. It is even more important when the main strategies to fulfil sales requirements are make-to-order (MTO) or assemble-to-order (ATO). Neither MTO or ATO companies keep finished products in stock which means that production level is driven by actual sales figures. Therefore, it is very important to have an available capacity as flexible as possible to meet sales requirements. Annualized hours (AH) is a work time control system that helps in increasing the flexibility of available capacity. The main rule of the AH system is to hire employees to work a certain number of hours on a yearly basis. Following some other pre-defined rules and limits, the AH system allows a company to adjust weekly available time in order to reduce idle capacity or to fulfil requirements that could mean overtime. This paper proposes a linear programming model to plan the operations using AH. This model has been implemented in a company that produces agricultural implements, showing that it is very simple to use. It is producing excellent practical results.     

Non-linear regression models for Approximate Bayesian Computation

Approximate Bayesian inference on the basis of summary statistics is well-suited to complex problems for which the likelihood is either mathematically or computationally intractable. However the methods that use rejection suffer from the curse of dimensionality when the number of summary statistics is increased. Here we propose a machine-learning approach to the estimation of the posterior density by introducing two innovations. The new method fits a nonlinear conditional heteroscedastic regression of the parameter on the summary statistics, and then adaptively improves estimation using importance sampling. The new algorithm is compared to the state-of-the-art approximate Bayesian methods, and achieves considerable reduction of the computational burden in two examples of inference in statistical genetics and in a queueing model.