Quality control of blood irradiation with a teletherapy unit: damage to stored red blood cells after cobalt-60 gamma irradiation

BACKGROUND: Previous publications have documented the damage caused to red blood cells (RBCs) irradiated with X-rays produced by a linear accelerator and with gamma rays derived from a Cs-137 source. The biologic effects on RBCs of gamma rays from a Co-60 source, however, have not been characterized. STUDY DESIGN AND METHODS: This study investigated the effect of 3000 and 4000 cGy on the in vitro properties of RBCs preserved with preservative solution and irradiated with a cobalt teletherapy unit. A thermal device equipped with a data acquisition system was used to maintain and monitor the blood temperature during irradiation. The device was rotated at 2 r.p.m. in the irradiation beam by means of an automated system. The spatial distribution of the absorbed dose over the irradiated volume was obtained with phantom and thermoluminescent dosimeters (TLDs). Levels of Hb, K+, and Cl- were assessed by spectrophotometric techniques over a period of 45 days. The change in the topology of the RBC membrane was investigated by flow cytometry. RESULTS: Irradiation caused significant changes in the extracellular levels of K+ and Hb and in the organizational structure of the phospholipid bilayer of the RBC membrane. Blood temperature ranged from 2 to 4 degrees C during irradiation. Rotation at 2 r.p.m. distributed the dose homogeneously (92%-104%) and did not damage the RBCs. CONCLUSIONS: The method used to store the blood bags during irradiation guaranteed that all damage caused to the cells was exclusively due to the action of radiation at the doses applied. It was demonstrated that prolonged storage of Co-60-irradiated RBCs results in loss of membrane phospholipids asymmetry, exposing phosphatidylserine (PS) on the cells` surface with a time and dose dependence, which can reduce the in vivo recovery of these cells. A time- and dose-dependence effect on the extracellular K+ and plasma-free Hb levels was also observed. The magnitude of all these effects, however, seems not to be clinically important and can support the storage of irradiated RBC units for at last 28 days.

Brain monoaminergic neurons and ventilatory control in vertebrates

Monoamines (noradrenaline (NA), adrenaline (AD), dopamine (DA) and serotonin (5-HT) are key neurotransmitters that are implicated in multiple physiological and pathological brain mechanisms, including control of respiration. The monoaminergic system is known to be widely distributed in the animal kingdom, which indicates a considerable degree of phylogenetic conservation of this system amongst vertebrates. Substantial progress has been made in uncovering the participation of the brain monoamines in the breathing regulation of mammals, since they are involved in the maturation of the respiratory network as well as in the modulation of its intrinsic and synaptic properties. On the other hand, for the non-mammalian vertebrates, most of the knowledge of central monoaminergic modulation in respiratory control, which is actually very little, has emerged from studies using anuran amphibians. This article reviews the available data on the role of brain monoaminergic systems in the control of ventilation in terrestrial vertebrates. Emphasis is given to the comparative aspects of the brain noradrenergic, adrenergic, dopaminergic and serotonergic neuronal groups in breathing regulation, after first briefly considering the distribution of monoaminergic neurons in the vertebrate brain. (C) 2008 Elsevier B.V. All rights reserved.

Supervisory control of wastewater treatment plants by combining principal component analysis and fuzzy c-means clustering

In this paper a methodology for integrated multivariate monitoring and control of biological wastewater treatment plants during extreme events is presented. To monitor the process, on-line dynamic principal component analysis (PCA) is performed on the process data to extract the principal components that represent the underlying mechanisms of the process. Fuzzy c-means (FCM) clustering is used to classify the operational state. Performing clustering on scores from PCA solves computational problems as well as increases robustness due to noise attenuation. The class-membership information from FCM is used to derive adequate control set points for the local control loops. The methodology is illustrated by a simulation study of a biological wastewater treatment plant, on which disturbances of various types are imposed. The results show that the methodology can be used to determine and co-ordinate control actions in order to shift the control objective and improve the effluent quality.

Pediculus humanus capitis infestations in the community: A pilot study into transmission, treatment and factors affecting control

Head lice (Pediculus humanus capitis) infestations affect schoolchildren worldwide, creating social, economic and health consequences for families. Problems with self-detection, chronic infestations and classroom transmission are compounded by increasing resistance of the lice to pediculicides. Public health strategies are based on limited research and little is known about transmission dynamics. Mismanagement and transmission in the general community are blamed for control failure. The purpose of this study was to explore community head-lice experience in Brisbane, Australia, and to identify critical factors underlying control failure. A home-based pilot survey used physical examination to verify transmission and treatment patterns which were self-reported by a group of trace-contact families in addition to other unconnected participants. The survey was enlarged to further compare therapy outcomes and suspected risk factors. The findings reinforce those of previous studies - that children attending school and early childhood centres, and subsequently their families, are most at risk of contracting pediculosis capitis, and some may carry lice for years. First-line (pediculicidal) treatment and even additional physical methods of hand-picking and fine-toothed combing usually fail to eradicate lice quickly and completely (overall cure-rate 39 per cent, n = 84 cases). Failures were linked to hair characteristics. Public education alone may not control pediculosis. Accurate diagnosis requires considerable experience; a strong case exists for returning to institutional surveillance.

Review and future directions in the modelling and control of continuous drum granulation

Many granulation plants operate well below design capacity, suffering from high recycle rates and even periodic instabilities. This behaviour cannot be fully predicted using the present models. The main objective of the paper is to provide an overview of the current status of model development for granulation processes and suggest future directions for research and development. The end-use of the models is focused on the optimal design and control of granulation plants using the improved predictions of process dynamics. The development of novel models involving mechanistically based structural switching methods is proposed in the paper. A number of guidelines are proposed for the selection of control relevant model structures. (C) 2002 Published by Elsevier Science B.V.

Caracterização de isolados de Corynespora cassiicola e avaliação da sensibilidade in vitro a fungicidas

Tese (doutorado)—Universidade de Brasília, Instituto de Ciências Biológicas, Departamento de Fitopatologia, Programa de Pós-Graduação em Fitopatologia, 2015.

Assessment of a strategy for the control of respiratory diseases in children

A programme for the control of respiratory diseases in children was conceived for the State of S. Paulo, Brazil, in 1986. Its progress thereafter and the epidemiology of the diseases concerned are examined. Apart from an inquiry into the 64 existing State local health authorities, a sample of 18,255 cases of children assisted by the programme at different levels, including both in-patient and outpatient care, is analysed. Each case record included information about identification (child, doctor and health facility), reasons for calling, diagnoses made and outcome of treatment. Further data were also sought from hospitals and from State mortality records. The programme was found to be poorly implemented in the State but, where implemented, it showed itself capable of resolving problems (only 0.5% of the cases could not be handled) as also of changing ongoing trends (more than 50% reduction in hospital admission rates). Individual assessment of each item of the programme indicated its bottlenecks. Regarding the epidemiology of respiratory diseases, it is observed that the major burden to health services comes from children aged less than five, and that the most important diseases are wheezing illnesses and pneumonia. Morevoer, they were found to be significantly associated (p = 0.000) so that a child in the community presenting wheezing diseases is 5 times more likely to develop pneumonia than a child with any other respiratory diagnosis. Similarly, among the under five deaths it was found that the risk for pneumonia is 3 times greater for children who died presenting wheezing diseases than it is for children with any other sort of diagnosis. In conclusion, the programme is deemed to be efficient and effective but its efficacy is marred by administrative flaws. The successful control of respiratory problems in childhood is related to a proper appreciation of the importance of wheezing diseases.

RFID together with multi-agent systems to control global value chains

Nowadays, the cooperative intelligent transport systems are part of a largest system. Transportations are modal operations integrated in logistics and, logistics is the main process of the supply chain management. The supply chain strategic management as a simultaneous local and global value chain is a collaborative/cooperative organization of stakeholders, many times in co-opetition, to perform a service to the customers respecting the time, place, price and quality levels. The transportation, like other logistics operations must add value, which is achieved in this case through compression lead times and order fulfillments. The complex supplier's network and the distribution channels must be efficient and the integral visibility (monitoring and tracing) of supply chain is a significant source of competitive advantage. Nowadays, the competition is not discussed between companies but among supply chains. This paper aims to evidence the current and emerging manufacturing and logistics system challenges as a new field of opportunities for the automation and control systems research community. Furthermore, the paper forecasts the use of radio frequency identification (RFID) technologies integrated into an information and communication technologies (ICT) framework based on distributed artificial intelligence (DAI) supported by a multi-agent system (MAS), as the most value advantage of supply chain management (SCM) in a cooperative intelligent logistics systems. Logistical platforms (production or distribution) as nodes of added value of supplying and distribution networks are proposed as critical points of the visibility of the inventory, where these technological needs are more evident.

Distributed energy resource short-term scheduling using signaled particle swarm optimization

Distributed Energy Resources (DER) scheduling in smart grids presents a new challenge to system operators. The increase of new resources, such as storage systems and demand response programs, results in additional computational efforts for optimization problems. On the other hand, since natural resources, such as wind and sun, can only be precisely forecasted with small anticipation, short-term scheduling is especially relevant requiring a very good performance on large dimension problems. Traditional techniques such as Mixed-Integer Non-Linear Programming (MINLP) do not cope well with large scale problems. This type of problems can be appropriately addressed by metaheuristics approaches. This paper proposes a new methodology called Signaled Particle Swarm Optimization (SiPSO) to address the energy resources management problem in the scope of smart grids, with intensive use of DER. The proposed methodology’s performance is illustrated by a case study with 99 distributed generators, 208 loads, and 27 storage units. The results are compared with those obtained in other methodologies, namely MINLP, Genetic Algorithm, original Particle Swarm Optimization (PSO), Evolutionary PSO, and New PSO. SiPSO performance is superior to the other tested PSO variants, demonstrating its adequacy to solve large dimension problems which require a decision in a short period of time.

Adaptable control for electrical generation at irregural wind speeds

The main aims of this work are the development and the validation of one generic algorithm to provide the optimal control of small power wind generators. That means up to 40 kW and blades with fixed pitch angle. This algorithm allows the development of controllers to fetch the wind generators at the desired operational point in variable operating conditions. The problems posed by the variable wind intensity are solved using the proposed algorithm. This is done with no explicit measure of the wind velocity, and so no special equipment or anemometer is required to compute or measure the wind velocity.

Genetic algorithm methodology applied to intelligent house control

In recent years the use of several new resources in power systems, such as distributed generation, demand response and more recently electric vehicles, has significantly increased. Power systems aim at lowering operational costs, requiring an adequate energy resources management. In this context, load consumption management plays an important role, being necessary to use optimization strategies to adjust the consumption to the supply profile. These optimization strategies can be integrated in demand response programs. The control of the energy consumption of an intelligent house has the objective of optimizing the load consumption. This paper presents a genetic algorithm approach to manage the consumption of a residential house making use of a SCADA system developed by the authors. Consumption management is done reducing or curtailing loads to keep the power consumption in, or below, a specified energy consumption limit. This limit is determined according to the consumer strategy and taking into account the renewable based micro generation, energy price, supplier solicitations, and consumers’ preferences. The proposed approach is compared with a mixed integer non-linear approach.

ANN based support for distributed energy resources scheduling in smart grids

The future scenarios for operation of smart grids are likely to include a large diversity of players, of different types and sizes. With control and decision making being decentralized over the network, intelligence should also be decentralized so that every player is able to play in the market environment. In the new context, aggregator players, enabling medium, small, and even micro size players to act in a competitive environment, will be very relevant. Virtual Power Players (VPP) and single players must optimize their energy resource management in order to accomplish their goals. This is relatively easy to larger players, with financial means to have access to adequate decision support tools, to support decision making concerning their optimal resource schedule. However, the smaller players have difficulties in accessing this kind of tools. So, it is required that these smaller players can be offered alternative methods to support their decisions. This paper presents a methodology, based on Artificial Neural Networks (ANN), intended to support smaller players’ resource scheduling. The used methodology uses a training set that is built using the energy resource scheduling solutions obtained with a reference optimization methodology, a mixed-integer non-linear programming (MINLP) in this case. The trained network is able to achieve good schedule results requiring modest computational means.

Computational intelligence applications for future power systems

Power system planning, control and operation require an adequate use of existing resources as to increase system efficiency. The use of optimal solutions in power systems allows huge savings stressing the need of adequate optimization and control methods. These must be able to solve the envisaged optimization problems in time scales compatible with operational requirements. Power systems are complex, uncertain and changing environments that make the use of traditional optimization methodologies impracticable in most real situations. Computational intelligence methods present good characteristics to address this kind of problems and have already proved to be efficient for very diverse power system optimization problems. Evolutionary computation, fuzzy systems, swarm intelligence, artificial immune systems, neural networks, and hybrid approaches are presently seen as the most adequate methodologies to address several planning, control and operation problems in power systems. Future power systems, with intensive use of distributed generation and electricity market liberalization increase power systems complexity and bring huge challenges to the forefront of the power industry. Decentralized intelligence and decision making requires more effective optimization and control techniques techniques so that the involved players can make the most adequate use of existing resources in the new context. The application of computational intelligence methods to deal with several problems of future power systems is presented in this chapter. Four different applications are presented to illustrate the promises of computational intelligence, and illustrate their potentials.

Cyber-ambient intelligent training of operators in power systems control centres

Cyber-Physical Systems and Ambient Intelligence are two of the most important and emerging paradigms of our days. The introduction of renewable sources gave origin to a completely different dimension of the distribution generation problem. On the other hand, Electricity Markets introduced a different dimension in the complexity, the economic dimension. Our goal is to study how to proceed with the Intelligent Training of Operators in Power Systems Control Centres, considering the new reality of Renewable Sources, Distributed Generation, and Electricity Markets, under the emerging paradigms of Cyber-Physical Systems and Ambient Intelligence. We propose Intelligent Tutoring Systems as the approach to deal with the intelligent training of operators in these new circumstances.