Standard goal programming with fuzzy hierarchies: a sequential approach

This paper proposes a pragmatic model for multi-objective decision-making processes involving clusters of objectives which have a decisional meaning for the decision maker (DM). We provide the DMs with a comfortable tool that allows them to express their preferences both by comparing criteria of the same cluster and via the comparison between the different clusters. In standard goal programming the importance of the goals is modeled by the introduction of preferential weights or/and the incorporation of pre-emptive priorities. However, in many cases the DM is not able to establish a precise preference structure. Even in the case of precise weights the solution does not match necessarily the relative weights or, in the case of precise pre-emptive priority, the result could be very restrictive. In order to overcome these drawbacks, in this paper the normalized unwanted deviations are interpreted in terms of achievement degrees of the goals and fuzzy relations are used to model the relative importance of the goals. Thus, we show how several methodologies from the fuzzy goal programming literature can be tailored for solving standard GP problems. We apply this new modeling to problems where there is a “natural” clustering between goals of the same class. We address this situation by solving two phases; in the first one each class is handled separately taking into account the hierarchy of their goals and, in the second phase, we integrate the results of the first phase and the imprecise hierarchy of the different classes. We formulate a new goal programming model called as sequential goal programming with fuzzy hierarchy model. Because many real situations involve decision making in this environment, our proposal can be a useful tool of broad application. A numerical example illustrates the methodology.     

Competence management in knowledge intensive organizations using consensual knowledge and ontologies

This article describes an architecture suitable for use in a competence management system for knowledge intensive organizations (KIOs). The underlying motivation for this work is to explore the practical problems of the use of codified knowledge in knowledge management systems (KMS) in KIOs. We explore some of the key issues associated with the use of tacit and codified knowledge in KMS, and describe an architecture based on an ontology-driven framework derived from collective and consensual knowledge that acts as a structure for a formal knowledge base. We describe, in outline, a prototype competence management system based on this architecture designed to support the management of competencies in a structured way. We conclude with some observations about our approach to the representation of knowledge in a KMS and its potential value to KIOs.     

Infiltrating the zebrafish swarm: design,implementation and experimental tests of a miniature robotic fish lure for fish–robot interaction studies

Robotic fish are nowadays developed for various types of research, such as bio-inspiredrobotics, biomimetics and animal behavior studies. In the context of our research on the social interactions of the zebrafish Danio Rerio, we developed a miniature robotic fish lure for direct underwater interaction with the living fish. This remotely controlled and waterproof device has a total length of 7.5 cm with the same size ratio as zebrafish and is able to beat its tail with different frequencies and amplitudes, while following the group of living animals using a mobile robot moving outside water that is coupled with the robotic lure using magnets. The robotic lure is also equipped with a rechargeable battery and can be used autonomously underwater for experiments of up to 1 h. We performed experiments with the robot moving inside an aquarium with living fish to analyze its impact on the zebrafish behavior. We found that the beating rate of the tail increased the attractiveness of the lure among the zebrafish shoal. We also demonstrated that the lure could influence a collective decision of the zebrafish shoal, the swimming direction, when moving with a constant linear speed inside a circular corridor. This new robotic fish design and the experimental results are promising for the field of fish–robot interaction.     

A novel methodology to predict urban traffic congestion with ensemble learning

Urban traffic congestion prediction is a very hot topic due to the environmental and economical impacts that currently implies. In this sense, to be able to predict bottlenecks and to provide alternatives to the circulation of vehicles becomes an essential task for traffic management. A novel methodology, based on ensembles of machine learning algorithms, is proposed to predict traffic congestion in this paper. In particular, a set of seven algorithms of machine learning has been selected to prove their effectiveness in the traffic congestion prediction. Since all the seven algorithms are able to address supervised classification, the methodology has been developed to be used as a binary classification problem. Thus, collected data from sensors located at the Spanish city of Seville are analyzed and models reaching up to 83 % are generated.     

Exploring factors related to users’ experience of public transport route choice: influence of context and users profiles

This exploratory study aims to achieve a better understanding of the users-related factors that affect the choice of routes in public transport (PT). We also look at what can motivate route and modes changes towards alternatives in a real situation. We investigated the experience of 19 users of PTs, using the critical incident technique (Flanagan in Psychol Bull 51(4):327, 1954). We asked participants to report incidents (i.e. situations) in cases they were very satisfied or dissatisfied with their choice. For both situations, the case of their usual route and case of an alternative were considered. A total of 91 incidents were collected and analysed using a multiple correspondences analysis. Additionally, users’ profiles were characterized and superposed to the analysis of incidents content. The main results are as follows. First, the user’s choice of PT route depends on the context (i.e. aim of the travel, time of day). Second, taking an alternative to the usual PT route or using a route combining different transport modes is determined by the context and by factors related to the pleasantness of the travel (e.g. to accompany a friend along the travel). Finally, depending on the user’s profile (i.e. combination of attitude towards PT and demographic variables), the factors taken into account to make the choice of a PT route are related to the efficiency or the pleasantness of the trip. These results show the importance of the contextual factors and the users’ profiles in route choice. They suggest that these factors should be further taken into account in new tools and services for mobility.     

Notes on spiking neural P systems and finite automata

Spiking neural P systems (in short, SN P systems) are membrane computing models inspired by the pulse coding of information in biological neurons. SN P systems with standard rules have neurons that emit at most one spike (the pulse) each step, and have either an input or output neuron connected to the environment. A variant known as SN P modules generalize SN P systems by using extended rules (more than one spike can be emitted each step) and a set of input and output neurons. In this work we continue relating SN P modules and finite automata. In particular, we amend and improve previous constructions for the simulatons of deterministic finite automata and state transducers. Our improvements reduce the number of neurons from three down to one, so our results are optimal. We also simulate finite automata with output, and we use these simulations to generate automatic sequences.     

Parallel simulation of Population Dynamics P systems: updates and roadmap

Population Dynamics P systems are a type of multienvironment P systems that serve as a formal modeling framework for real ecosystems. The accurate simulation of these probabilistic models, e.g. with Direct distribution based on Consistent Blocks Algorithm, entails large run times. Hence, parallel platforms such as GPUs have been employed to speedup the simulation. In 2012, the first GPU simulator of PDP systems was presented. However, it was able to run only randomly generated PDP systems. In this paper, we present current updates made on this simulator, involving an input modu le for binary files and an output module for CSV files. Finally, the simulator has been experimentally validated with a real ecosystem model, and its performance has been tested with two high-end GPUs: Tesla C1060 and K40.     

Analytical error analysis of Clifford gates by the fault-path tracer method

We estimate the success probability of quantum protocols composed of Clifford operations in the presence of Pauli errors. Our method is derived from the fault-point formalism previously used to determine the success rate of low-distance error correction codes. Here we apply it to a wider range of quantum protocols and identify circuit structures that allow for efficient calculation of the exact success probability and even the final distribution of output states. As examples, we apply our method to the Bernstein–Vazirani algorithm and the Steane [[7,1,3]] quantum error correction code and compare the results to Monte Carlo simulations.