1,720,968 research outputs found
Naïve Bayes ant colony optimization for designing high dimensional experiments
Full text linkIn a large number of experimental problems, high dimensionality of the search area and economical constraints can severely limit the number of experimental points that can be tested. Within these constraints, classical optimization techniques perform poorly, in particular, when little a priori knowledge is available. In this work we investigate the possibility of combining approaches from statistical modeling and bio-inspired algorithms to effectively explore a huge search space, sampling only a limited number of experimental points. To this purpose, we introduce a novel approach, combining ant colony optimization (ACO) and naive Bayes classifier (NBC) that is, the naive Bayes ant colony optimization (NACO) procedure. We compare NACO with other similar approaches developing a simulation study. We then derive the NACO procedure with the goal to design artificial enzymes with no sequence homology to the extant one. Our final aim is to mimic the natural fold of 200 amino acids 1AGY serine esterase from Fusarium solani
Response improvement in complex experiments by co-information composite likelihood optimization
Full text linkWe propose an adaptive procedure for improving the response outcomes of complex combinatorial experiments. New experiment batches are chosen by minimizing the co-information composite likelihood (COIL) objective function, which is derived by coupling importance sampling and composite likelihood principles. We show convergence of the best experiment within each batch to the globally optimal experiment in finite time, and carry out simulations to assess the convergence behavior as the design space size increases. The procedure is tested as a new enzyme engineering protocol in an experiment with a design space size of order 107. © 2013 Springer Science+Business Media New York
Using accounting information to predict aggressive tax location decisions by European groups
No full textAlthough locating a company in a tax haven is not illegal per se, it is likely to be part of a scheme purported to erode the tax base or to shift profits to less-taxed jurisdictions. For this reason, this type of location decision is usually targeted by anti-avoidance laws, that can take the form either of specific rules or general standards that, ex-post, sanction or limit the location decision. However, rules entail higher drafting costs and are easy to circumvent whereas standards entail more uncertainty costs. The goal of this paper is to illustrate that the risk of aggressive location decisions can be predicted ex-ante using publicly available data and that this prediction can be used by tax authorities. In the paper, we do two things. First, we use publicly available accounting data for the period 2015-2019 on 4031 group ultimate owners (GUO) of active listed companies resident in one of the 27 European Union countries to predict the probability that these companies would have at least a subsidiary in a tax haven, by spring 2021, as well as the intensity in the use of tax havens. Second, we discuss how this prediction can be used by tax authorities in the context of a new administrative preventive approach that complements the traditional legal approach. This approach can increase welfare by reducing uncertainty, thus increasing investments and economic growth
A computer-aided methodology for the optimization of electrostatic separation processes in recycling
Full text linkThe rapid growth of technological products has led to an increasing volume of waste electrical and electronic equipments (WEEE), which could represent a valuable source of critical raw materials. However, current mechanical separation processes for recycling are typically poorly operated, making it impossible to modify the process parameters as a function of the materials under treatment, thus resulting in untapped separation potentials. Corona electrostatic separation (CES) is one of the most popular processes for separating fine metal and nonmetal particles derived from WEEE. In order to optimize the process operating conditions (i.e., variables) for a given multi-material mixture under treatment, several technological and economical criteria should be jointly considered. This translates into a complex optimization problem that can be hardly solved by a purely experimental approach. As a result, practitioners tend to assign process parameters by few experiments based on a small material sample and to keep these parameters fixed during the process life-cycle. The use of computer experiments for parameter optimization is a mostly unexplored area in this field. In this work, a computer-aided approach is proposed to the problem of optimizing the operational parameters in CES processes. Three metamodels, developed starting from a multi-body simulation model of the process physics, are presented and compared by means of a numerical and simulation study. Our approach proves to be an effective framework to optimize the CES process performance. Furthermore, by comparing the predicted response surfaces of the metamodels, additional insight into the process behavior over the operating region is obtained
Designing lead optimisation of MMP-12 Inhibitors
Full text linkThe design of new molecules with desired properties is in general a very difficult problem, involving heavy experimentation with high investment of resources and possible negative impact on the environment. The standard approach consists of iteration among formulation, synthesis, and testing cycles, which is a very long and laborious process. In this paper we address the so-called lead optimisation process by developing a new strategy to design experiments and modelling data, namely, the evolutionary model-based design for optimisation (EDO). This approach is developed on a very small set of experimental points, which change in relation to the response of the experimentation according to the principle of evolution and insights gained through statistical models. This new procedure is validated on a data set provided as test environment by Pickett et al. (2011), and the results are analysed and compared to the genetic algorithm optimisation (GAO) as a benchmark. The very good performance of the EDO approach is shown in its capacity to uncover the optimum value using a very limited set of experimental points, avoiding unnecessary experimentation
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
A land-use perspective for birdstrike risk assessment: The attraction risk index
Full text linkCollisions between aircraft and birds, birdstrikes, pose a serious threat to aviation safety.
The occurrence of these events is influenced by land-uses in the surroundings of airports.
Airports located in the same region might have different trends for birdstrike risk, due to differences
in the surrounding habitats. Here we developed a quantitative tool that assesses
the risk of birdstrike based on the habitats within a 13-km buffer from the airport. For this purpose,
we developed Generalized Linear Models (GLMs) with binomial distribution to estimate
the contribution of habitats to wildlife use of the study area, depending on season.
These GLMs predictions were combined to the flight altitude of birds within the 13-km buffer,
the airport traffic pattern and the severity indices associated with impacts. Our approach
was developed at Venice Marco Polo International airport (VCE), located in northeast Italy
and then tested at Treviso Antonio Canova International airport (TSF), which is 20 km
inland. Results from the two airports revealed that both the surrounding habitats and the
season had a significant influence to the pattern of risk. With regard to VCE, agricultural
fields, wetlands and urban areas contributed most to the presence of birds in the study
area. Furthermore, the key role of distance of land-uses from the airport on the probability of
presence of birds was highlighted. The reliability of developed risk index was demonstrated
since at VCE it was significantly correlated with bird strike rate. This study emphasizes the
importance of the territory near airports and the wildlife use of its habitats, as factors in need
of consideration for birdstrike risk assessment procedures. Information on the contribution
of habitats in attracting birds, depending on season, can be used by airport managers and
local authorities to plan specific interventions in the study area in order to lower the risk
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