Fractional model for malaria transmission under control strategies

We study a fractional model for malaria transmission under control strategies.Weconsider the integer order model proposed by Chiyaka et al. (2008) in [15] and modify it to become a fractional order model. We study numerically the model for variation of the values of the fractional derivative and of the parameter that models personal protection, b. From observation of the figures we conclude that as b is increased from 0 to 1 there is a corresponding decrease in the number of infectious humans and infectious mosquitoes, for all values of α. This means that this result is invariant for variation of fractional derivative, in the values tested. These results are in agreement with those obtained in Chiyaka et al.(2008) [15] for α = 1.0 and suggest that our fractional model is epidemiologically wellposed.

Self-mated tribological systems based on multilayer micro/nanocrystalline CVD diamond coatings

The tribological response of multilayer micro/nanocrystalline diamond coatings grown by the hot filament CVD technique is investigated. These multigrade systems were tailored to comprise a starting microcrystalline diamond (MCD) layer with high adhesion to a silicon nitride (Si3N4) ceramic substrate, and a top nanocrystalline diamond (NCD) layer with reduced surface roughness. Tribological tests were carried out with a reciprocating sliding configuration without lubrication. Such composite coatings exhibit a superior critical load before delamination (130–200 N), when compared to the mono- (60–100 N) and bilayer coatings (110 N), considering ∼10 µm thick films. Regarding the friction behaviour, a short-lived initial high friction coefficient was followed by low friction regimes (friction coefficients between 0.02 and 0.09) as a result of the polished surfaces tailored by the tribological solicitation. Very mild to mild wear regimes (wear coefficient values between 4.1×10−8 and 7.7×10−7 mm3 N−1 m−1) governed the wear performance of the self-mated multilayer coatings when subjected to high-load short-term tests (60–200 N; 2 h; 86 m) and medium-load endurance tests (60 N; 16 h; 691 m).

Self-similarity principle: the reduced description of randomness

A new general fitting method based on the Self-Similar (SS) organization of random sequences is presented. The proposed analytical function helps to fit the response of many complex systems when their recorded data form a self-similar curve. The verified SS principle opens new possibilities for the fitting of economical, meteorological and other complex data when the mathematical model is absent but the reduced description in terms of some universal set of the fitting parameters is necessary. This fitting function is verified on economical (price of a commodity versus time) and weather (the Earth’s mean temperature surface data versus time) and for these nontrivial cases it becomes possible to receive a very good fit of initial data set. The general conditions of application of this fitting method describing the response of many complex systems and the forecast possibilities are discussed.

Comparing the schedulers and power saving strategies with SPARTS

We have developed SPARTS, a simulator of a generic embedded real-time device. It is designed to be extensible to accommodate different task properties, scheduling algorithms and/or hardware models for the wide variety of applications. SPARTS was developed to help the community investigate the behaviour of the real-time embedded systems and to quantify the associated constraints/overheads.

Self-adaptive combination of global tabu search and local search for nonlinear equations

Solving systems of nonlinear equations is a very important task since the problems emerge mostly through the mathematical modelling of real problems that arise naturally in many branches of engineering and in the physical sciences. The problem can be naturally reformulated as a global optimization problem. In this paper, we show that a self-adaptive combination of a metaheuristic with a classical local search method is able to converge to some difficult problems that are not solved by Newton-type methods.

Scheduling a cutting and treatment stainless steel sheet line with self-management capabilities

With advancement in computer science and information technology, computing systems are becoming increasingly more complex with an increasing number of heterogeneous components. They are thus becoming more difficult to monitor, manage, and maintain. This process has been well known as labor intensive and error prone. In addition, traditional approaches for system management are difficult to keep up with the rapidly changing environments. There is a need for automatic and efficient approaches to monitor and manage complex computing systems. In this paper, we propose an innovative framework for scheduling system management by combining Autonomic Computing (AC) paradigm, Multi-Agent Systems (MAS) and Nature Inspired Optimization Techniques (NIT). Additionally, we consider the resolution of realistic problems. The scheduling of a Cutting and Treatment Stainless Steel Sheet Line will be evaluated. Results show that proposed approach has advantages when compared with other scheduling systems

Meta-heuristics self-parameterization in a multi-agent scheduling system using case-based reasoning

This paper proposes a novel agent-based approach to Meta-Heuristics self-configuration. Meta-heuristics are algorithms with parameters which need to be set up as efficient as possible in order to unsure its performance. A learning module for self-parameterization of Meta-heuristics (MH) in a Multi-Agent System (MAS) for resolution of scheduling problems is proposed in this work. The learning module is based on Case-based Reasoning (CBR) and two different integration approaches are proposed. A computational study is made for comparing the two CBR integration perspectives. Finally, some conclusions are reached and future work outlined.

Optimização das fugas pela boca durante o CPAP nasal: banda submentoniana versus máscara facial

Introdução: O CPAP nasal é o tratamento de eleição para os pacientes com Síndrome da Apneia Obstrutiva do Sono (SAOS). Com a máscara nasal podem ocorrer fugas de ar pela boca, que podem por em causa a aderência do paciente ao tratamento devido muitas vezes ao desconforto que provocam, ao aumento do trabalho respiratório e por afectarem a qualidade do sono. Objectivos: Este estudo tem como principal objectivo verificar a eficácia da banda submentoniana e da máscara facial na correcção das fugas pela boca em pacientes com SAOS. Métodos e Participantes: Uma amostra de conveniência de 15 pacientes (8 homens) com SAOS e a fazerem CPAP com máscara nasal, foi recrutada. Foram divididos em dois grupos A e B, onde no grupo A se colocou a banda submentoniana e no grupo B se alterou a interface para máscara facial. Medidas e Resultados: As variáveis avaliadas neste estudo foram as fugas, o IAH, o percentil 95 da pressão de tratamento, a Sa,O2 e os efeitos adversos das duas intervenções. O nível de fugas reduziu no grupo A de 38±11,27 para 24,55±14,30L/min (p=0,002) e no grupo B de 34,34±16,50 para 18,51±16,22L/min (p=0,008). O IAH aumentou no grupo B de 2,60±2,13 para 4,41±3,88 (p=0,016). Relativamente ao percentil 95 da pressão de tratamento aumentou nos dois grupos (Grupo A de 10,15±2,63 para 12,08±2,45cmH2O (p=0,008) e no Grupo B 10,51±1,88 para 12,64±1,29cmH2O (p=0,008)). A Sa,O2 mínima aumentou e o tempo<90% diminui no grupo A com p=0,008, p=0,031, respectivamente. Quanto ao uso auto-reportado verificaram-se poucos efeitos adversos, salientando-se apenas a facilidade de colocação da banda submentoniana, a secura da boca nos dois grupos, a pressão no queixo provocada pela banda e a dor no dorso do nariz provocada pela máscara facial. Conclusão: Ambas as estratégias provaram ser mais eficazes a reduzir a fuga que a máscara nasal. Foram bem toleradas e com poucos efeitos adversos.

On-line self-healing of circuits implemented on reconfigurable FPGAs

To boost logic density and reduce per unit power consumption SRAM-based FPGAs manufacturers adopted nanometric technologies. However, this technology is highly vulnerable to radiation-induced faults, which affect values stored in memory cells, and to manufacturing imperfections. Fault tolerant implementations, based on Triple Modular Redundancy (TMR) infrastructures, help to keep the correct operation of the circuit. However, TMR is not sufficient to guarantee the safe operation of a circuit. Other issues like module placement, the effects of multi- bit upsets (MBU) or fault accumulation, have also to be addressed. In case of a fault occurrence the correct operation of the affected module must be restored and/or the current state of the circuit coherently re-established. A solution that enables the autonomous restoration of the functional definition of the affected module, avoiding fault accumulation, re-establishing the correct circuit state in real-time, while keeping the normal operation of the circuit, is presented in this paper.

A framework for self-healing radiation-tolerant implementations on reconfigurable FPGAs

To increase the amount of logic available in SRAM-based FPGAs manufacturers are using nanometric technologies to boost logic density and reduce prices. However, nanometric scales are highly vulnerable to radiation-induced faults that affect values stored in memory cells. Since the functional definition of FPGAs relies on memory cells, they become highly prone to this type of faults. Fault tolerant implementations, based on triple modular redundancy (TMR) infrastructures, help to keep the correct operation of the circuit. However, TMR is not sufficient to guarantee the safe operation of a circuit. Other issues like the effects of multi-bit upsets (MBU) or fault accumulation, have also to be addressed. Furthermore, in case of a fault occurrence the correct operation of the affected module must be restored and the current state of the circuit coherently re-established. A solution that enables the autonomous correct restoration of the functional definition of the affected module, avoiding fault accumulation, re-establishing the correct circuit state in realtime, while keeping the normal operation of the circuit, is presented in this paper.

A self-healing real-time system based on run-time self-reconfiguration

The new generations of SRAM-based FPGA (field programmable gate array) devices are the preferred choice for the implementation of reconfigurable computing platforms intended to accelerate processing in real-time systems. However, FPGA's vulnerability to hard and soft errors is a major weakness to robust configurable system design. In this paper, a novel built-in self-healing (BISH) methodology, based on run-time self-reconfiguration, is proposed. A soft microprocessor core implemented in the FPGA is responsible for the management and execution of all the BISH procedures. Fault detection and diagnosis is followed by repairing actions, taking advantage of the dynamic reconfiguration features offered by new FPGA families. Meanwhile, modular redundancy assures that the system still works correctly

A new approach to assess defragmentation strategies in dynamically reconfigurable FPGAs

Fragmentation on dynamically reconfigurable FPGAs is a major obstacle to the efficient management of the logic space in reconfigurable systems. When resource allocation decisions have to be made at run-time a rearrangement may be necessary to release enough contiguous resources to implement incoming functions. The feasibility of run-time relocation depends on the processing time required to set up rearrangements. Moreover, the performance of the relocated functions should not be affected by this process or otherwise the whole system performance, and even its operation, may be at risk. Relocation should take into account not only specific functional issues, but also the FPGA architecture, since these two aspects are normally intertwined. A simple and fast method to assess performance degradation of a function during relocation and to speed up the defragmentation process, based on previous function labelling and on the application of the Euclidian distance concept, is proposed in this paper.

Negotiation mechanism for self-organized scheduling system with collective intelligence

Current Manufacturing Systems challenges due to international economic crisis, market globalization and e-business trends, incites the development of intelligent systems to support decision making, which allows managers to concentrate on high-level tasks management while improving decision response and effectiveness towards manufacturing agility. This paper presents a novel negotiation mechanism for dynamic scheduling based on social and collective intelligence. Under the proposed negotiation mechanism, agents must interact and collaborate in order to improve the global schedule. Swarm Intelligence (SI) is considered a general aggregation term for several computational techniques, which use ideas and inspiration from the social behaviors of insects and other biological systems. This work is primarily concerned with negotiation, where multiple self-interested agents can reach agreement over the exchange of operations on competitive resources. Experimental analysis was performed in order to validate the influence of negotiation mechanism in the system performance and the SI technique. Empirical results and statistical evidence illustrate that the negotiation mechanism influence significantly the overall system performance and the effectiveness of Artificial Bee Colony for makespan minimization and on the machine occupation maximization.

Double power laws, fractals and self-similarity

Power law (PL) distributions have been largely reported in the modeling of distinct real phenomena and have been associated with fractal structures and self-similar systems. In this paper, we analyze real data that follows a PL and a double PL behavior and verify the relation between the PL coefficient and the capacity dimension of known fractals. It is to be proved a method that translates PLs coefficients into capacity dimension of fractals of any real data.

Race-to-halt energy saving strategies

Energy consumption is one of the major issues for modern embedded systems. Early, power saving approaches mainly focused on dynamic power dissipation, while neglecting the static (leakage) energy consumption. However, technology improvements resulted in a case where static power dissipation increasingly dominates. Addressing this issue, hardware vendors have equipped modern processors with several sleep states. We propose a set of leakage-aware energy management approaches that reduce the energy consumption of embedded real-time systems while respecting the real-time constraints. Our algorithms are based on the race-to-halt strategy that tends to run the system at top speed with an aim to create long idle intervals, which are used to deploy a sleep state. The effectiveness of our algorithms is illustrated with an extensive set of simulations that show an improvement of up to 8% reduction in energy consumption over existing work at high utilization. The complexity of our algorithms is smaller when compared to state-of-the-art algorithms. We also eliminate assumptions made in the related work that restrict the practical application of the respective algorithms. Moreover, a novel study about the relation between the use of sleep intervals and the number of pre-emptions is also presented utilizing a large set of simulation results, where our algorithms reduce the experienced number of pre-emptions in all cases. Our results show that sleep states in general can save up to 30% of the overall number of pre-emptions when compared to the sleep-agnostic earliest-deadline-first algorithm.