Quantitative Analysis and Performance Study of Ant Colony Optimization Models Applied to Multi-Mode Resource Constraint Project Scheduling Problem

Constraint Satisfaction Problems (CSP) belongs to this kind of traditional NP-hard problems with a high impact in both, research and industrial domains. However, due to the complexity that CSP problems exhibit, researchers are forced to use heuristic algorithms for solving the problems in a reasonable time. One of the most famous heuristic al- gorithms is Ant Colony Optimization (ACO) algorithm. The possible utilization of ACO algorithms to solve CSP problems requires the de- sign of a decision graph where the ACO is executed. Nevertheless, the classical approaches build a graph where the nodes represent the vari- able/value pairs and the edges connect those nodes whose variables are different. In order to solve this problem, a novel ACO model have been recently designed. The goal of this paper is to analyze the performance of this novelty algorithm when solving Multi-Mode Resource-Constraint Satisfaction Problems. Experimental results reveals that the new ACO model provides competitive results whereas the number of pheromones created in the system is drastically reduced

Comparative study of pheromone control heuristics in ACO algorithms for solving RCPSP problems

Constraint Satisfaction Problems (CSP) belong to a kind of traditional NP-hard problems with a high impact on both research and industrial domains. The goal of these problems is to find a feasible assignment for a group of variables where a set of imposed restrictions is satisfied. This family of NP-hard problems demands a huge amount of computational resources even for their simplest cases. For this reason, different heuristic methods have been studied so far in order to discover feasible solutions at an affordable complexity level. This paper elaborates on the application of Ant Colony Optimization (ACO) algorithms with a novel CSP-graph based model to solve Resource-Constrained Project Scheduling Problems (RCPSP). The main drawback of this ACO-based model is related to the high number of pheromones created in the system. To overcome this issue we propose two adaptive Oblivion Rate heuristics to control the number of pheromones: the first one, called Dynamic Oblivion Rate, takes into account the overall number of pheromones produced in the system, whereas the second one inspires from the recently contributed Coral Reef Optimization (CRO) solver. A thorough experimental analysis has been carried out using the public PSPLIB library, and the obtained results have been compared to those of the most relevant contributions from the related literature. The performed experiments reveal that the Oblivion Rate heuristic removes at least 79% of the pheromones in the system, whereas the ACO algorithm renders statistically better results than other algorithmic counterparts from the literature

Adaptive algorithm for increasing image rejection ratio in Low-IF receivers

An adaptive method for compensating mismatch effect on an I/Q demodulation is presented. It is based on the correlation between the desired and image band that appears when there is a mismatch in any branch. The proposed algorithm is specially intended for narrowband low-IF receivers. No calibration source is neccesary. Up to 91 dB of image rejection ratio is achieved with narrow-bandwidth signals (up to 6.25% of the sampling frequency)

A novel adaptive density-based ACO algorithm with minimal encoding redundancy for clustering problems

In the so-called Big Data paradigm descriptive analytics are widely conceived as techniques and models aimed at discovering knowledge within unlabeled datasets (e.g. patterns, similarities, etc) of utmost help for subsequent predictive and prescriptive methods. One of these techniques is clustering, which hinges on different multi-dimensional measures of similarity between unsupervised data instances so as to blindly collect them in groups of clusters. Among the myriad of clustering approaches reported in the literature this manuscript focuses on those relying on bio-inspired meta-heuristics, which have been lately shown to outperform traditional clustering schemes in terms of convergence, adaptability and parallelization. Specifically this work presents a new clustering approach based on the processing fundamentals of the Ant Colony Optimization (ACO) algorithm, i.e. stigmergy via pheromone trails and progressive construction of solutions through a graph. The novelty of the proposed scheme beyond previous research on ACO-based clustering lies on a significantly pruned graph that not only minimizes the representation redundancy of the problem at hand, but also allows for an embedded estimation of the number of clusters within the data. However, this approach imposes a modified ant behavior so as to account for the optimality of entire paths rather than that of single steps within the graph. Simulation results over conventional datasets will evince the promising performance of our approach and motivate further research aimed at its applicability to real scenarios

A Biomathematical Model of Tumor Response to Radioimmunotherapy with PDL1 and CTLA4

There is evidence of synergy between radiotherapy and immunotherapy. Radiotherapy can increase liberation of tumor antigens, causing activation of antitumor T-cells. This effect can be boosted with immunotherapy. Radioimmunotherapy has potential to increase tumor control rates. Biomathematical models of response to radioimmunotherapy may help on understanding of the mechanisms affecting response, and assist clinicians on the design of optimal treatment strategies. In this work we present a biomathematical model of tumor response to radioimmunotherapy. The model uses the linear-quadratic response of tumor cells to radiation (or variation of it), and builds on previous developments to include the radiation-induced immune effect. We have focused this study on the combined effect of radiotherapy and PDL1/CTLA4 therapies. The model can fit preclinical data of volume dynamics and control obtained with different dose fractionations and PDL1/CTLA4. A biomathematical study of optimal combination strategies suggests that a good understanding of the involved biological delays, the biokinetics of the immunotherapy drug, and the interplay between them, may be of paramount importance to design optimal radioimmunotherapy schedules. Biomathematical models like the one we present can help to interpret experimental data on the synergy between radiotherapy and immunotherapy, and to assist in the design of more effective treatments

Using the clustering coefficient to guide a genetic-based communities finding algorithm

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-23878-9_20Proceedings of 12th International Conference IDEAL, Norwich, UK, September 7-9, 2011.We describe an approach taken for automatically associating entries from an on-line encyclopedia with concepts in an ontology or a lexical semantic network. It has been tested with the Simple English Wikipedia and WordNet, although it can be used with other resources. The accuracy in disambiguating the sense of the encyclopedia entries reaches 91.11% (83.89% for polysemous words). It will be applied to enriching ontologies with encyclopedic knowledge

A Simulation Method for the Computation of the E

We propose a set of numerical methods for the computation of the frequency-dependent eff ective primary wave velocity of heterogeneous rocks. We assume the rocks' internal microstructure is given by micro-computed tomography images. In the low/medium frequency regime, we propose to solve the acoustic equation in the frequency domain by a Finite Element Method (FEM). We employ a Perfectly Matched Layer to truncate the computational domain and we show the need to repeat the domain a su cient number of times to obtain accurate results. To make this problem computationally tractable, we equip the FEM with non-fitting meshes and we precompute multiple blocks of the sti ffness matrix. In the high-frequency range, we solve the eikonal equation with a Fast Marching Method. Numerical results con rm the validity of the proposed methods and illustrate the e ffect of density, porosity, and the size and distribution of the pores on the e ective compressional wave velocity

The human CD53 gene, coding for a four transmembrane domain protein, maps to chromosomal region 1p13

The rat OX44/CD53 protein is the prototypic member of a"novel" family of proteins. These proteins are characterizedby four highly hydrophobic transmembrane domains, twosmall extracellular domains, one of which is extensively N-glycosylated,and both the amino and the carboxy terminus intracytoplasmic.This work has been supported by grants from Fundación Ramón Areces and CICYT (SAL90-0209 and SAL91-0043) to P.A.L. and FIS(92-0889) to S.R.CS

Factors associated with compliance with physical activity recommendations among adolescents in Huesca.

BACKGROUND: Schools have been identified as environments of choice for physical activity promotion. This study examines factors associated with compliance with objectively assessed physical activity recommendations for early adolescents taking part in “Sigue la Huella”, a school-based intervention guided by a social ecological framework and Self-Determination Theory (Deci & Ryan, 2002). METHODS: A total of 200 students (108 boys) aged 12-13 years (M = 12.16; SD = ± 0.51), wore accelerometers during a 7-day period and completed a questionnaire. Participants were considered compliant to the recommendations if their moderate to vigorous physical activity, averaged over 7 days, was =60 minutes a day. RESULTS: 57.4% of boys and 9.9% of girls met recommendations. In a mixed logistic regression model, compliance was higher among boys and students attending private schools, and lower for obese students. Compliance was also associated with higher perceptions of physical competence, higher perceptions of autonomy in physical education, greater importance attached to physical education and less sedentary behavior. CONCLUSIONS: Assessed objectively, gender differences in compliance with physical activity recommendations were greater than expected. Self-Determination Theory emerged as a useful framework to identify motivational factors that can be addressed in school-based physical activity interventions and programs for early adolescents