Immune cell changes in response to a swimming training session during a 24-h recovery period

Understanding the impact of training sessions on the immune response is crucial for the adequate periodization of training, to prevent both a negative influence on health and a performance impairment of the athlete. This study evaluated acute systemic immune cell changes in response to an actual swimming session, during a 24-h recovery period, controlling for sex, menstrual cycle phases, maturity, and age group. Competitive swimmers (30 females, 15 ± 1.3 years old; and 35 males, 16.5 ± 2.1 years old) performed a high-intensity training session. Blood samples were collected before, immediately after, 2 h after, and 24 h after exercise. Standard procedures for the assessment of leukogram by automated counting (Coulter LH 750, Beckman) and lymphocytes subsets by flow cytometry (FACS Calibur BD, Biosciences) were used. Subjects were grouped according to competitive age groups and pubertal Tanner stages. Menstrual cycle phase was monitored. The training session induced neutrophilia, lymphopenia, and a low eosinophil count, lasting for at least 2 h, independent of sex and maturity. At 24 h postexercise, the acquired immunity of juniors (15-17 years old), expressed by total lymphocytes and total T lymphocytes (CD3+), was not fully recovered. This should be accounted for when planning a weekly training program. The observed lymphopenia suggests a lower immune surveillance at the end of the session that may depress the immunity of athletes, highlighting the need for extra care when athletes are exposed to aggressive environmental agents such as swimming pools

Transmission price simulator in a liberalized electricity market

This paper presents a software tool (SIM_CMTP) that solves congestion situations and evaluates the taxes to be paid to the transmission system by market agents. SIM_CMTP provides users with a set of alternative methods for cost allocation and enables the definition of specific rules, according to each market and/or situation needs. With these characteristics, SIM_CMTP can be used as an operation aid for Transmission System Operator (TSO) or Independent System Operator (ISO). Due to its openness, it can also be used as a decision-making support tool for evaluating different options of market rules in competitive market environment, guarantying the economic sustainability of the transmission system.

MASCEM: electricity markets simulation with strategic agents

Electricity markets are complex environments, involving numerous entities trying to obtain the best advantages and profits while limited by power-network characteristics and constraints.1 The restructuring and consequent deregulation of electricity markets introduced a new economic dimension to the power industry. Some observers have criticized the restructuring process, however, because it has failed to improve market efficiency and has complicated the assurance of reliability and fairness of operations. To study and understand this type of market, we developed the Multiagent Simulator of Competitive Electricity Markets (MASCEM) platform based on multiagent simulation. The MASCEM multiagent model includes players with strategies for bid definition, acting in forward, day-ahead, and balancing markets and considering both simple and complex bids. Our goal with MASCEM was to simulate as many market models and player types as possible. This approach makes MASCEM both a short- and mediumterm simulation as well as a tool to support long-term decisions, such as those taken by regulators. This article proposes a new methodology integrated in MASCEM for bid definition in electricity markets. This methodology uses reinforcement learning algorithms to let players perceive changes in the environment, thus helping them react to the dynamic environment and adapt their bids accordingly.

A data-mining-based methodology for transmission expansion planning

In recent decades, all over the world, competition in the electric power sector has deeply changed the way this sector’s agents play their roles. In most countries, electric process deregulation was conducted in stages, beginning with the clients of higher voltage levels and with larger electricity consumption, and later extended to all electrical consumers. The sector liberalization and the operation of competitive electricity markets were expected to lower prices and improve quality of service, leading to greater consumer satisfaction. Transmission and distribution remain noncompetitive business areas, due to the large infrastructure investments required. However, the industry has yet to clearly establish the best business model for transmission in a competitive environment. After generation, the electricity needs to be delivered to the electrical system nodes where demand requires it, taking into consideration transmission constraints and electrical losses. If the amount of power flowing through a certain line is close to or surpasses the safety limits, then cheap but distant generation might have to be replaced by more expensive closer generation to reduce the exceeded power flows. In a congested area, the optimal price of electricity rises to the marginal cost of the local generation or to the level needed to ration demand to the amount of available electricity. Even without congestion, some power will be lost in the transmission system through heat dissipation, so prices reflect that it is more expensive to supply electricity at the far end of a heavily loaded line than close to an electric power generation. Locational marginal pricing (LMP), resulting from bidding competition, represents electrical and economical values at nodes or in areas that may provide economical indicator signals to the market agents. This article proposes a data-mining-based methodology that helps characterize zonal prices in real power transmission networks. To test our methodology, we used an LMP database from the California Independent System Operator for 2009 to identify economical zones. (CAISO is a nonprofit public benefit corporation charged with operating the majority of California’s high-voltage wholesale power grid.) To group the buses into typical classes that represent a set of buses with the approximate LMP value, we used two-step and k-means clustering algorithms. By analyzing the various LMP components, our goal was to extract knowledge to support the ISO in investment and network-expansion planning.

From antimicrobial agents to health promotion

A emergência de multiresistência apresentada por microrganismos é um dos grandes desafios que enfrentam actualmente os profissionais de Saúde e a população em geral. Os factores que contribuem para o desenvolvimento de resistência a antibióticos na comunidade podem ser categorizados como comportamentais ou ambientais/políticas. O objectivo deste trabalho foi caracterizar a situação actual na visão dos Pais de alunos do pré-escolar e 1º ciclo. De modo a avaliar as necessidades de intervenção e as actividades a serem desenvolvidas, um instrumento para estudar os hábitos e comportamentos adoptados na utilização de antibióticos, foi adaptado, validado e aplicado numa amostra piloto.

Seller, buyer and VPP agents behavior on a simulated electricity market

Competitive electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is an electricity market simulator able to model market players and simulate their operation in the market. As market players are complex entities, having their characteristics and objectives, making their decisions and interacting with other players, a multi-agent architecture is used and proved to be adequate. MASCEM players have learning capabilities and different risk preferences. They are able to refine their strategies according to their past experience (both real and simulated) and considering other agents’ behavior. Agents’ behavior is also subject to its risk preferences.

MV producers and consumers agents characterization with DSM techniques

This paper consist in the establishment of a Virtual Producer/Consumer Agent (VPCA) in order to optimize the integrated management of distributed energy resources and to improve and control Demand Side Management DSM) and its aggregated loads. The paper presents the VPCA architecture and the proposed function-based organization to be used in order to coordinate the several generation technologies, the different load types and storage systems. This VPCA organization uses a frame work based on data mining techniques to characterize the costumers. The paper includes results of several experimental tests cases, using real data and taking into account electricity generation resources as well as consumption data.

Self-managing agents for dynamic scheduling in manufacturing

The main purpose of this paper is to propose a Multi-Agent Autonomic and Bio-Inspired based framework with selfmanaging capabilities to solve complex scheduling problems using cooperative negotiation. Scheduling resolution requires the intervention of highly skilled human problem-solvers. This is a very hard and challenging domain because current systems are becoming more and more complex, distributed, interconnected and subject to rapidly changing. A natural Autonomic Computing (AC) evolution in relation to Current Computing is to provide systems with Self-Managing ability with a minimum human interference.

Commercial agents portfolio optimization in electricity markets

As it is well known, competitive electricity markets require new computing tools for power companies that operate in retail markets in order to enhance the management of its energy resources. During the last years there has been an increase of the renewable penetration into the micro-generation which begins to co-exist with the other existing power generation, giving rise to a new type of consumers. This paper develops a methodology to be applied to the management of the all the aggregators. The aggregator establishes bilateral contracts with its clients where the energy purchased and selling conditions are negotiated not only in terms of prices but also for other conditions that allow more flexibility in the way generation and consumption is addressed. The aggregator agent needs a tool to support the decision making in order to compose and select its customers' portfolio in an optimal way, for a given level of profitability and risk.

Dynamic power- and failure-aware cloud resources allocation for Sets of independent tasks

Cloud computing is increasingly being adopted in different scenarios, like social networking, business applications, scientific experiments, etc. Relying in virtualization technology, the construction of these computing environments targets improvements in the infrastructure, such as power-efficiency and fulfillment of users’ SLA specifications. The methodology usually applied is packing all the virtual machines on the proper physical servers. However, failure occurrences in these networked computing systems can induce substantial negative impact on system performance, deviating the system from ours initial objectives. In this work, we propose adapted algorithms to dynamically map virtual machines to physical hosts, in order to improve cloud infrastructure power-efficiency, with low impact on users’ required performance. Our decision making algorithms leverage proactive fault-tolerance techniques to deal with systems failures, allied with virtual machine technology to share nodes resources in an accurately and controlled manner. The results indicate that our algorithms perform better targeting power-efficiency and SLA fulfillment, in face of cloud infrastructure failures.

Nanofiltration of Cork Wastewaters and Their Possible Use in Leather Industry as Tanning Agents

Cork processing wastewater is a very complex mixture of vegetal extracts and has, among other natural compounds, a very high content of phenolic/tannic colloidal matter that is responsible for severe environmental problems. In the present work, the concentration of this wastewater by nanofiltration was investigated with the aim of producing a cork tannin concentrate to be utilized in tanning. Permeation results showed that the permeate fluxes are controlled by both osmotic pressure and fouling/gel layer phenomena, leading to a rapid decrease of permeate fluxes with the concentration factor. The rejection coefficients to organic matter were higher than 95%, indicating that nanofiltration has a very good ability to concentrate the tannins and produce a permeate stream depleted from organic matter. The cork tannin concentrate obtained by nanofiltration and evaporation had total solids concentration of 34.8 g/l. The skins tanned by this concentrate were effectively converted to leather with a shrinking temperature of 7 degrees C.

Molecular imaging agents for detection of β-amyloid plaques in Alzheimer’s disease

The formation of amyloid structures is a neuropathological feature that characterizes several neurodegenerative disorders, such as Alzheimer´s and Parkinson´s disease. Up to now, the definitive diagnosis of these diseases can only be accomplished by immunostaining of post mortem brain tissues with dyes such Thioflavin T and congo red. Aiming at early in vivo diagnosis of Alzheimer´s disease (AD), several amyloid-avid radioprobes have been developed for b-amyloid imaging by positron emission tomography (PET) and single-photon emission computed tomography (SPECT). The aim of this paper is to present a perspective of the available amyloid imaging agents, special those that have been selected for clinical trials and are at the different stages of the US Food and Drugs Administration (FDA) approval.

Diagnosis of Tinea pedis and onychomycosis in patients from Portuguese National Institute of Health: a four-year study

ABSTRACT - Tinea pedis and onychomycosis are two rather diverse clinical manifestations of superficial fungal infections, and their etiologic agents may be dermatophytes, non-dermatophyte moulds or yeasts. This study was designed to statistically describe the data obtained as results of analysis conducted during a four year period on the frequency of Tinea pedis and onychomycosis and their etiologic agents. A questionnaire was distributed from 2006 to 2010 and answered by 186 patients, who were subjected to skin and/or nail sampling. Frequencies of the isolated fungal species were cross-linked with the data obtained with the questionnaire, seeking associations and predisposing factors. One hundred and sixty three fungal isolates were obtained, 24.2% of which composed by more than one fungal species. Most studies report the two pathologies as caused primarily by dermatophytes, followed by yeasts and lastly by non-dermatophytic moulds. Our study does not challenge this trend. We found a frequency of 15.6% of infections caused by dermatophytes (with a total of 42 isolates) of which T. rubrum was the most frequent species (41.4%). There was no significant association (p >0.05) among visible injury and the independent variables tested, namely age, gender, owning pet, education, swimming pools attendance, sports activity and clinical information. Unlike other studies, the variables considered did not show the expected influence on dermatomycosis of the lower limbs. It is hence necessary to conduct further studies to specifically identify which variables do in fact influence such infections.

Xanthenedione derivatives, new promising antioxidant and acetylcholinesterase inhibitor agents

Natural and synthetic xanthone derivatives are well-known for their ability to act as antioxidants and/or enzyme inhibitors. This paper aims to present a successful synthetic methodology towards xanthenedione derivatives and the study of their aromatization to xanthones. Additionally their ability to reduce Fe(III), to scavenge DPPH radicals and to inhibit AChE was evaluated. The results demonstrated that xanthenedione derivative 5e, bearing a catechol unit, showed higher reduction capacity than BHT and similar to quercetin, strong DPPH scavenging activity (EC50 = 3.79 ± 0.06 μM) and it was also showed to be a potent AChEI (IC50 = 31.0 ± 0.09 μM) when compared to galantamine (IC50 = 211.8 ± 9.5 μM).

Adaptive learning in agents behaviour: a framework for electricity markets simulation

Electricity markets are complex environments with very particular characteristics. A critical issue regarding these specific characteristics concerns the constant changes they are subject to. This is a result of the electricity markets’ restructuring, which was performed so that the competitiveness could be increased, but it also had exponential implications in the increase of the complexity and unpredictability in those markets scope. The constant growth in markets unpredictability resulted in an amplified need for market intervenient entities in foreseeing market behaviour. The need for understanding the market mechanisms and how the involved players’ interaction affects the outcomes of the markets, contributed to the growth of usage of simulation tools. Multi-agent based software is particularly well fitted to analyze dynamic and adaptive systems with complex interactions among its constituents, such as electricity markets. This dissertation presents ALBidS – Adaptive Learning strategic Bidding System, a multiagent system created to provide decision support to market negotiating players. This system is integrated with the MASCEM electricity market simulator, so that its advantage in supporting a market player can be tested using cases based on real markets’ data. ALBidS considers several different methodologies based on very distinct approaches, to provide alternative suggestions of which are the best actions for the supported player to perform. The approach chosen as the players’ actual action is selected by the employment of reinforcement learning algorithms, which for each different situation, simulation circumstances and context, decides which proposed action is the one with higher possibility of achieving the most success. Some of the considered approaches are supported by a mechanism that creates profiles of competitor players. These profiles are built accordingly to their observed past actions and reactions when faced with specific situations, such as success and failure. The system’s context awareness and simulation circumstances analysis, both in terms of results performance and execution time adaptation, are complementary mechanisms, which endow ALBidS with further adaptation and learning capabilities.