Auto-parametrização de meta-heurísticas para escalonamento dinâmico

Este artigo aborda o problema da parametrização de Técnicas de Optimização Inspiradas na Biologia (BIT - Biological Inspired Optimization Techniques), também conhecidas como Meta-heurísticas, considerando a importância que estas técnicas têm na resolução de situações de mundo real, sujeitas a perturbações externas. É proposto um módulo de aprendizagem com o objectivo de permitir que um Sistema Multi-Agente (SMA) para Escalonamento seleccione automaticamente uma Metaheurística e escolha a parametrização a usar no processo de optimização. Para o módulo de aprendizagem foi usado o Raciocínio baseado em Casos (RBC), permitindo ao sistema aprender a partir da experiência acumulada na resolução de problemas similares. Através da análise dos resultados obtidos é possível concluir acerca das vantagens da sua utilização.

Case-based reasoning for self-optimizing behavior

In this paper we present a Self-Optimizing module, inspired on Autonomic Computing, acquiring a scheduling system with the ability to automatically select a Meta-heuristic to use in the optimization process, so as its parameterization. Case-based Reasoning was used so the system may be able of learning from the acquired experience, in the resolution of similar problems. From the obtained results we conclude about the benefit of its use.

Collective intelligence on dynamic manufacturing scheduling optimization

Swarm Intelligence (SI) is a growing research field of Artificial Intelligence (AI). SI is the general term for several computational techniques which use ideas and get inspiration from the social behaviours of insects and of other animals. This paper presents hybridization and combination of different AI approaches, like Bio-Inspired Techniques (BIT), Multi-Agent systems (MAS) and Machine Learning Techniques (ML T). The resulting system is applied to the problem of jobs scheduling to machines on dynamic manufacturing environments.

Mecanismo de negociação para sistema de escalonamento dinâmico

Este artigo propõe um Mecanismo de Negociação para Escalonamento Dinâmico com recurso a Swarm Intelligence (SI). No Mecanismo de Negociação, os agentes devem competir para obter um plano de escalamento global. SI é o termo geral para várias técnicas computacionais que retiram ideias e inspiração nos comportamentos sociais de insectos e outros animais. Este artigo propõe uma abordagem híbrida de diferentes conceitos da Inteligência Artificial (IA), como SI, Negociação em Sistemas Multi-Agente (SMA) e Técnicas de Aprendizagem Automática (AA). Este trabalho concentra a sua atenção na negociação, processo através do qual múltiplos agentes auto-interessados podem chegar a acordo através da troca competitiva de recursos.

Meta-heuristics tunning using CBR for dynamic scheduling

This paper addresses the problem of Biological Inspired Optimization Techniques (BIT) parameterization, considering the importance of this issue in the design of BIT especially when considering real world situations, subject to external perturbations. A learning module with the objective to permit a Multi-Agent Scheduling System to automatically select a Meta-heuristic and its parameterization to use in the optimization process is proposed. For the learning process, Casebased Reasoning was used, allowing the system to learn from experience, in the resolution of similar problems. Analyzing the obtained results we conclude about the advantages of its use.

Self-optimization aspects for dynamic scheduling

Scheduling is a critical function that is present throughout many industries and applications. A great need exists for developing scheduling approaches that can be applied to a number of different scheduling problems with significant impact on performance of business organizations. A challenge is emerging in the design of scheduling support systems for manufacturing environments where dynamic adaptation and optimization become increasingly important. At this scenario, self-optimizing arise as the ability of the agent to monitor its state and performance and proactively tune itself to respond to environmental stimuli.

Self-optimizing through CBR learning

In this paper, we foresee the use of Multi-Agent Systems for supporting dynamic and distributed scheduling in Manufacturing Systems. We also envisage the use of Autonomic properties in order to reduce the complexity of managing systems and human interference. By combining Multi-Agent Systems, Autonomic Computing, and Nature Inspired Techniques we propose an approach for the resolution of dynamic scheduling problem, with Case-based Reasoning Learning capabilities. The objective is to permit a system to be able to automatically adopt/select a Meta-heuristic and respective parameterization considering scheduling characteristics. From the comparison of the obtained results with previous results, we conclude about the benefits of its use.

Smart offices and intelligent decision rooms

Nowadays computing technology research is focused on the development of Smart Environments. Following that line of thought several Smart Rooms projects were developed and their appliances are very diversified. The appliances include projects in the context of workplace or everyday living, entertainment, play and education. These appliances envisage to acquire and apply knowledge about the environment state in order to reason about it so as to define a desired state for its inhabitants and perform adaptation adaptation to these desires and therefore improving their involvement and satisfaction with that environment.

Context-aware emotion-based model for group decision making

Involving groups in important management processes such as decision making has several advantages. By discussing and combining ideas, counter ideas, critical opinions, identified constraints, and alternatives, a group of individuals can test potentially better solutions, sometimes in the form of new products, services, and plans. In the past few decades, operations research, AI, and computer science have had tremendous success creating software systems that can achieve optimal solutions, even for complex problems. The only drawback is that people don’t always agree with these solutions. Sometimes this dissatisfaction is due to an incorrect parameterization of the problem. Nevertheless, the reasons people don’t like a solution might not be quantifiable, because those reasons are often based on aspects such as emotion, mood, and personality. At the same time, monolithic individual decisionsupport systems centered on optimizing solutions are being replaced by collaborative systems and group decision-support systems (GDSSs) that focus more on establishing connections between people in organizations. These systems follow a kind of social paradigm. Combining both optimization- and socialcentered approaches is a topic of current research. However, even if such a hybrid approach can be developed, it will still miss an essential point: the emotional nature of group participants in decision-making tasks. We’ve developed a context-aware emotion based model to design intelligent agents for group decision-making processes. To evaluate this model, we’ve incorporated it in an agent-based simulator called ABS4GD (Agent-Based Simulation for Group Decision), which we developed. This multiagent simulator considers emotion- and argument based factors while supporting group decision-making processes. Experiments show that agents endowed with emotional awareness achieve agreements more quickly than those without such awareness. Hence, participant agents that integrate emotional factors in their judgments can be more successful because, in exchanging arguments with other agents, they consider the emotional nature of group decision making.

Post-surgical wound infections involving Enterobacteriaceae with reduced susceptibility to ß-lactams in two Portuguese hospitals

The post-surgical period is often critical for infection acquisition. The combination of patient injury and environmental exposure through breached skin add risk to pre-existing conditions such as drug or depressed immunity. Several factors such as the period of hospital staying after surgery, base disease, age, immune system condition, hygiene policies, careless prophylactic drug administration and physical conditions of the healthcare centre may contribute to the acquisition of a nosocomial infection. A purulent wound can become complicated whenever antimicrobial therapy becomes compromised. In this pilot study, we analysed Enterobacteriaceae strains, the most significant gram-negative rods that may occur in post-surgical skin and soft tissue infections (SSTI) presenting reduced β-lactam susceptibility and those presenting extended-spectrum β-lactamases (ESBL). There is little information in our country regarding the relationship between β-lactam susceptibility, ESBL and development of resistant strains of microorganisms in SSTI. Our main results indicate Escherichia coli and Klebsiella spp. are among the most frequent enterobacteria (46% and 30% respectively) with ESBL production in 72% of Enterobacteriaceae isolates from SSTI. Moreover, coinfection occurred extensively, mainly with Pseudomonas aeruginosa and Methicillin-resistant Staphylococcus aureus (18% and 13%, respectively). These results suggest future research to explore if and how these associations are involved in the development of antibiotic resistance.

A 5GHz/1.8V CMOS Active Balun Integrated with LNA

The development of high performance monolithic RF front-ends requires innovative RF circuit design to make the best of a good technology. A fully differential approach is usually preferred, due to its well-known properties. Although the differential approach must be preserved inside the chip, there are cases where the input signal is single-ended such as RF image filters and IF filters in a RF receiver. In these situations, a stage able to convert single-ended into differential signals (balun) is needed. The most cited topology, which is capable of providing high gain, consists on a differential stage with one of the two inputs grounded. Unfortunately, this solution has some drawbacks when implemented monolithically. This work presents the design and simulated results of an innovative high-performance monolithic single to differential converter, which overcomes the limitations of the circuits.The integration of the monolithic active balun circuit with an LNA on a 0.18μm CMOS process is also reported. The circuits presented here are aimed at 802.11a. Section 2 describes the balun circuit and section 3 presents its performance when it is connected to a conventional single-ended LNA. Section 4 shows the simulated performance results focused at phase/amplitude balance and noise figure. Finally, the last section draws conclusions and future work.

Modelling and segmentation of the vocal tract during speech production by using deformable models in magnetic resonance images

The first and second authors would like to thank the support of the PhD grants with references SFRH/BD/28817/2006 and SFRH/PROTEC/49517/2009, respectively, from Fundação para a Ciência e Tecnol ogia (FCT). This work was partially done in the scope of the project “Methodologies to Analyze Organs from Complex Medical Images – Applications to Fema le Pelvic Cavity”, wi th reference PTDC/EEA- CRO/103320/2008, financially supported by FCT.

Dynamic network data envelopment analysis for university hospitals evaluation

ABSTRACT OBJECTIVE To develop an assessment tool to evaluate the efficiency of federal university general hospitals. METHODS Data envelopment analysis, a linear programming technique, creates a best practice frontier by comparing observed production given the amount of resources used. The model is output-oriented and considers variable returns to scale. Network data envelopment analysis considers link variables belonging to more than one dimension (in the model, medical residents, adjusted admissions, and research projects). Dynamic network data envelopment analysis uses carry-over variables (in the model, financing budget) to analyze frontier shift in subsequent years. Data were gathered from the information system of the Brazilian Ministry of Education (MEC), 2010-2013. RESULTS The mean scores for health care, teaching and research over the period were 58.0%, 86.0%, and 61.0%, respectively. In 2012, the best performance year, for all units to reach the frontier it would be necessary to have a mean increase of 65.0% in outpatient visits; 34.0% in admissions; 12.0% in undergraduate students; 13.0% in multi-professional residents; 48.0% in graduate students; 7.0% in research projects; besides a decrease of 9.0% in medical residents. In the same year, an increase of 0.9% in financing budget would be necessary to improve the care output frontier. In the dynamic evaluation, there was progress in teaching efficiency, oscillation in medical care and no variation in research. CONCLUSIONS The proposed model generates public health planning and programming parameters by estimating efficiency scores and making projections to reach the best practice frontier.

Impact of corporate social responsibility in the European pharmaceutical market

The objective of this master thesis is to evaluate the impact of CSR measures in the financial performance of the European pharmaceutical industry. By definition, CSR measures is quantified as corporate social disclosure considering the published CSR keywords on the annual reports of the selected companies, over four fiscal years (2010-2013). The financial performance of the companies were measured as return on assets (ROA) and Tobin’s Q. In order to defend the hypothesis developed, a multivariate regression is performed. The results obtained show no significant impact on the financial performance of a company nor in the short-time, nor in the long-time. Moreover, by comparison with other studies, it was possible to conclude that the financial performance is differently affected when considering different industries.

Reporting Income Taxes in the Banking Sector In Angola - Accounting, Tax Regulation and Financial Reporting Evidences

This research provides an insight into income taxes reporting in Angola, based on hand collected data from the annual reports of banks. Empirical studies on Angolan companies are scarce, in part due to the limited access to data. The results show that income taxes’ reporting has improved over the years 2010-2013, becoming more reliable and understandable. The Angolan Government is boosting the economic growth through tax benefits in the investment in public debt, which cause a reduction in the banks’ effective tax rate. The new income tax law will reduce the statutory tax rate from 2015 onwards and change the taxable income, resulting in shifting the focus to promoting private investment.