A finite volume–finite difference method with a stiff ordinary differential equation solver for advection–diffusion–reaction equation

A model for atmospheric pollutant transport is proposed considering an advection–diffusion–reaction equation. A splitting method is used to decouple the advection, diffusion and reaction parts. A scheme based on finite volume, finite difference and backward differentiation formula is used for solving an atmospheric transport-chemistry problem.     

DEMORS: A hybrid multi-objective optimization algorithm using differential evolution and rough set theory for constrained problems

The aim of this paper is to show how the hybridization of a multi-objective evolutionary algorithm (MOEA) and a local search method based on the use of rough set theory is a viable alternative to obtain a robust algorithm able to solve difficult constrained multi-objective optimization problems at a moderate computational cost. This paper extends a previously published MOEA [Hernández-Díaz AG, Santana-Quintero LV, Coello Coello C, Caballero R, Molina J. A new proposal for multi-objective optimization using differential evolution and rough set theory. In: 2006 genetic and evolutionary computation conference (GECCO’2006). Seattle, Washington, USA: ACM Press; July 2006], which was limited to unconstrained multi-objective optimization problems. Here, the main idea is to use this sort of hybrid approach to approximate the Pareto front of a constrained multi-objective optimization problem while performing a relatively low number of fitness function evaluations. Since in real-world problems the cost of evaluating the objective functions is the most significant, our underlying assumption is that, by aiming to minimize the number of such evaluations, our MOEA can be considered efficient. As in its previous version, our hybrid approach operates in two stages: in the first one, a multi-objective version of differential evolution is used to generate an initial approximation of the Pareto front. Then, in the second stage, rough set theory is used to improve the spread and quality of this initial approximation. To assess the performance of our proposed approach, we adopt, on the one hand, a set of standard bi-objective constrained test problems and, on the other hand, a large real-world problem with eight objective functions and 160 decision variables. The first set of problems are solved performing 10,000 fitness function evaluations, which is a competitive value compared to the number of evaluations previously reported in the specialized literature for such problems. The real-world problem is solved performing 250,000 fitness function evaluations, mainly because of its high dimensionality. Our results are compared with respect to those generated by NSGA-II, which is a MOEA representative of the state-of-the-art in the area.     

Intensive combined modality therapy including low-dose TBI in high-risk Ewing's Sarcoma Patients

Twenty-four high-risk Ewing's sarcoma patients were treatedf on an intensive combined modality protocol including low-dose fractionated total body irradiation (TBI) and autologous bone marrow infusion (ABMI). Twenty patients (83%) achieved a complete clinical response to the primary and/or metastatic sites following induction therapy. The median disease-free interval was 18 months, and nine patients remain disease-free with a follow-up of 22 to 72 months. Local failure as a manifestation of initial relapse occurred in only three patients (15%), each having synchronous distant failure. Eight patients failed initially with only distant metastases, usually within 1-2 years following a complete clinical response. Two patients with a single metastasis were again rendered disease-free and remain free from second relapse with 18 and 30 months follow-up. No other relapsed patient was able to be rendered disease-free, and most died of progressive disease within 6 to 12 months of relapse. Two patterns of granulocyte recovery following consolidative therapy (include TBI) and ABMI were recognized. Seventeen patients reached a total granulocyte count of >500 cells/mm3 within 4 weeks of ABMI (early graulocyte recovery), while seven patients required >4 weeks from ABMI (late granulocyte recovery). The time of platelet recovery (>50,000/mm3) was different for the groups with early and late granulocyte recovery (25 days vs. 54 days, p <.001). Six of seven patients with late granulocyte recovery received locl high-dose irratiation to >1/2 pelvis prior to bone marrow storage. Patients with late recovery did not tolerate maintenance chemotherapy. However, there was no difference in disease-free and overall survival, when compaing the groups with early and late granulocyte recovery. We conclude that these high-risk Ewing's sarcoma patients remain a poor-prognosis group in spite of intensive combined modality therapy include low-dose TBI. The control of microscopic systemic disease remains the major challenge to improving the cure rate. A new combined modality protocol with high-dose 'therapeutic' TBI (800 rad/2 fractions) is being used and the protocol design is outlined.     

Knowledge-based spatiotemporal linear abstraction

We present a theoretical framework and a case study for reusing the same conceptual and computational methodology for both temporal abstraction and linear (unidimensional) space abstraction, in a domain (evaluation of traffic-control actions) significantly different from the one (clinical medicine) in which the method was originally used. The method, known asknowledge-based temporal abstraction, abstracts high-level concepts and patterns from time-stamped raw data using a formal theory of domain-specific temporal-abstraction knowledge. We applied this method, originally used to interpret time-oriented clinical data, to the domain of traffic control, in which the monitoring task requires linear pattern matching along both space and time. First we reused the method for creation of unidimensional spatial abstractions over highways, given sensor measurements along each highway measured at the same time point. Second, we reused the method to create temporal abstractions of the traffic behaviour, for the same space segments, but during consecutive time points. We defined the corresponding temporal-abstraction and spatial-abstraction domain-specific knowledge. Our results suggest that (1) the knowledge-base temporal-abstraction method is reusable over time and unidimensional space as well as over significantly different domains; (2) the method can be generalised into a knowledge-based linear-abstraction method, which solves tasks requiring abstraction of data along any linear distance measure; and (3) a spatiotemporal-abstraction method can be assembled, from two copies of the generalised method and a spatial-decomposition mechanism, and is applicable to tasks requiring abstraction of time-oriented data into meaningful spatiotemporal patterns over a linear, decomposable space, such as traffic over a set of highways.     

Multi-objective optimization with an adaptive resonance theory-based estimation of distribution algorithm

The introduction of learning to the search mechanisms of optimization algorithms has been nominated as one of the viable approaches when dealing with complex optimization problems, in particular with multi-objective ones. One of the forms of carrying out this hybridization process is by using multi-objective optimization estimation of distribution algorithms (MOEDAs). However, it has been pointed out that current MOEDAs have an intrinsic shortcoming in their model-building algorithms that hamper their performance. In this work, we put forward the argument that error-based learning, the class of learning most commonly used in MOEDAs is responsible for current MOEDA underachievement. We present adaptive resonance theory (ART) as a suitable learning paradigm alternative and present a novel algorithm called multi-objective ART-based EDA (MARTEDA) that uses a Gaussian ART neural network for model-building and a hypervolume-based selector as described for the HypE algorithm. In order to assert the improvement obtained by combining two cutting-edge approaches to optimization an extensive set of experiments are carried out. These experiments also test the scalability of MARTEDA as the number of objective functions increases.     

Implementation of best manufacturing practices using logic models and system dynamics: project design and project assessment views

In today’s global competitive environment, the need for continuous improvement is a matter of considerable importance within manufacturing enterprises. To this end, project managers, and managers in general, design and assess different projects with the purpose of achieving efficient processes, reducing costs and waste, increasing product and service quality, developing new products and services, enhancing customer relationship management, optimising enterprise resources, and so on. However, it is well-known that managing enterprise resources in order to accomplish effective completion of projects is a complex task to carry out. Furthermore, it has been recognised that the way staff actually understands the purpose of a project, the way they perform different project activities, and how they are able to influence project design and assessment are key factors for influencing the success of a project. This paper presents a systemic methodology to design and assess projects more effectively and efficiently based on program logic models and system dynamics with the aim of facilitating a clear understanding of the needs, purposes, goals, activities and tasks of a project among its stakeholders towards achieving success.     

I3WSN: Industrial Intelligent Wireless Sensor Networks for indoor environments

Sensor Web Enablement (SWE) technologies have been successfully applied to a great variety of outdoor scenarios but, in practical terms, little effort has been applied for indoor environments, and even less in the field of industrial applications. This article presents an intelligent SWE application for an indoor and industrial scenario, with the aim of improving and increasing the levels of human safety. The base low-level architecture is built on top of wireless sensor networks (WSN) connected to a Sensor Observation Service (SOS). Higher layers in the architecture include services that make real time decisions based on the collected data. Both simulation and experimental results are presented. The paper shows the viability of our approach in terms of performance, scalability, modularity and safety.     

Distributed associative memory for use in scene analysis

A distributed associative memory system which is ideal for scene analysis is described. Recall of associated patterns using incomplete originals is made possible by the use of a distributed storage mechanism and a novel recall procedure. The memory is shown to store associations between patterns more efficiently than a conventional file store. The paper describes the memory structure, the recall process and its storage abilities, as well as an example of its implementation in hardware.     

Bovine somatotropin dose titration in lactating dairy ewes. 3. Treatment interval.

Seventy-four lactating dairy ewes were injected with recombinant bovine somatotropin (bST; sometribove) in a sustained-release formulation. Ewes received 0, 80, 160, or 240 mg of bST/14 d at the end of 2, 4, and 6 wk postlambing, after which injections were withheld for a 28-d period. Response of milk production to bST presented a positive linear contrast and a negative quadratic contrast. After the final injection of bST (d 0), an increase in daily milk production was observed until d +9. A continuous decrease followed with milk production returned to control levels on d +11, +18, and +20, for 80, 160, and 240 mg of bST/14 d. Milk protein content decreased gradually until d +8 before it returned to control levels on d +12, +14 and +14, for 80, 160, and 240 mg of bST/14 d. Levels of bST and IGF-I in plasma presented positive and highly significant linear contrasts and negative quadratics which were significant only for bST. Relative to the evolution of milk production, the evolution of IGF-I levels in plasma was more similar than that of bST, although the correlation of each measure with milk production was high and highly significant. It was concluded that the period between successive injections should be shorter the lower the dose employed; however, a 14-d period seemed to correspond correctly for the dosages and hormone formulation tested.