Application of logistic regression for fault analysis in an industrial printing process

A statistical technique for fault analysis in industrial printing is reported. The method specifically deals with binary data, for which the results of the production process fall into two categories, rejected or accepted. The method is referred to as logistic regression, and is capable of predicting future fault occurrences by the analysis of current measurements from machine parts sensors. Individual analysis of each type of fault can determine which parts of the plant have a significant influence on the occurrence of such faults; it is also possible to infer which measurable process parameters have no significant influence on the generation of these faults. Information derived from the analysis can be helpful in the operator's interpretation of the current state of the plant. Appropriate actions may then be taken to prevent potential faults from occurring. The algorithm is being implemented as part of an applied self-learning expert system.

Identifying and investigating pesticide application types to promote a more sustainable pesticide use: the case of smallholders in Boyacá, Colombia

The present paper investigates pesticide application types adopted by smallholder potato producers in the Department of Boyacá , Colombia. In this region, environmental, health and adverse economic effects due to pesticide mis- or over-use respectively have been observed. Firstly, pesticide application types were identified based on input-effectiveness. Secondly, their determinants of adoption were investigated. Finally suggestions were given to develop intervention options for transition towards a more sustainable pesticide use. Three application types were identified for fungicide and insecticide. The types differed in terms of input (intensity of pesticide application), effect (damage control), frequency of application, average quantity applied per application, chemical class, and productivity. Then, the determinants of different pesticide application types were investigated with a multinomial logistic regression approach and applying the integrative agent centred (IAC) framework. The area of the plot, attendance at training sessions and educational and income levels were among the most relevant determinants. The analysis suggested that better pesticide use could be fostered to reduce pesticide-related risks in the region. Intervention options were outlined, which may help in targeting this issue. They aim not only at educating farmers, but to change their social and institutional context, by involving other agents of the agricultural system (i.e. pesticide producers), facilitating new institutional settings (i.e. cooperatives) and targeting social dynamics (i.e. conformity to social norms).

Ensemble projections for wine production in the Douro Valley of Portugal

Wine production is largely governed by atmospheric conditions, such as air temperature and precipitation, together with soil management and viticultural/enological practices. Therefore, anthropogenic climate change is likely to have important impacts on the winemaking sector worldwide. An important winemaking region is the Portuguese Douro Valley, which is known by its world-famous Port Wine. The identification of robust relationships between atmospheric factors and wine parameters is of great relevance for the region. A multivariate linear regression analysis of a long wine production series (1932–2010) reveals that high rainfall and cool temperatures during budburst, shoot and inflorescence development (February-March) and warm temperatures during flowering and berry development (May) are generally favourable to high production. The probabilities of occurrence of three production categories (low, normal and high) are also modelled using multinomial logistic regression. Results show that both statistical models are valuable tools for predicting the production in a given year with a lead time of 3–4 months prior to harvest. These statistical models are applied to an ensemble of 16 regional climate model experiments following the SRES A1B scenario to estimate possible future changes. Wine production is projected to increase by about 10 % by the end of the 21st century, while the occurrence of high production years is expected to increase from 25 % to over 60 %. Nevertheless, further model development will be needed to include other aspects that may shape production in the future. In particular, the rising heat stress and/or changes in ripening conditions could limit the projected production increase in future decades.

Self-congruence, functional congruence, and destination choice

This study's purpose is to investigate the effects of self-congruence and functional congruence on tourists' destination choice. The present research contributes to the gap in the consumer behavior literature by examining the relationships among self-congruence, functional congruence, and destination choice. Based on a sample of 367 British residents, the three research hypotheses are tested using multinomial logistic regression analysis. The study results suggest that a tourist's destination choice is influenced strongly by functional congruence, but not by self-congruence. The article closes with theoretical and managerial implications as well as future research directions.