alpha-Co(OH)(2)/carbon nanofoam composite as electrochemical capacitor electrode operating at 2 V in aqueous medium

In this work, alpha-Co(OH)(2) is electrodeposited onto carbon nanofoam forming a composite electrode operating in a potential window of 2 V in aqueous medium. Prior to electrodeposition, the carbon nanofoam substrate is subjected to a functionalization process, which leads to an increase of about 40% in its specific capacitance value. Formation of cobalt hydroxide clusters onto the functionalized carbon nanofoam by pulse electrodeposition further enhances the specific capacitance of the electrode. The combination of these factors with an enlarged working potential window, results in a material with specific capacitance close to 300 F g(-1) at current density of 1 A g(-1), considering the total mass loading of the composite. This suggests the potential application of the prepared composites in high energy density electrochemical supercapacitors. (c) 2015 Elsevier B.V. All rights reserved.

Reply to: Comments on “Particle Swarm Optimization with Fractional-Order Velocity”

We agree with Ling-Yun et al. [5] and Zhang and Duan comments [2] about the typing error in equation (9) of the manuscript [8]. The correct formula was initially proposed in [6, 7]. The formula adopted in our algorithms discussed in our papers [1, 3, 4, 8] is, in fact, the following: ...

Gelatin particle indirect agglutination test for serodiagnosis of schistosomiasis: comparative study with enzyme-linked immunosorbent assay

A new serological test, the gelatin particle agglutination test (GPAT), was used for the serodiagnosis of schistosomiasis mansoni. This technique showed the sensitivity (90.6%) and specificity (97.8%) close to those of enzyme-linked immunosorbent assay. The GPAT can be easily and rapidly performed without specialized equipment, by using lyophilized antigen-coated gelatin particles. The test also seems to be useful for mass screening of Schistosoma infection in field conditions.

Long term follow-up and patterns of response of ALT in patients with chronic hepatitis NANB/C treated with recombinant interferon-alpha

The response to interferon treatment in chronic hepatitis NANB/C has usually been classified as complete, partial or absent, according to the behavior of serum alanine aminotransferase (ALT). However, a more detailed observation of the enzymatic activity has shown that the patterns may be more complex. The aim of this study was to describe the long term follow-up and patterns of ALT response in patients with chronic hepatitis NANB/C treated with recombinant interferon-alpha. A follow-up of 6 months or more after interferon-a was achieved in 44 patients. We have classified the serum ALT responses into six patterns and the observed frequencies were as follows: I. Long term response = 9 (20.5%); II. Normalization followed by persistent relapse after IFN = 7 (15.9%); III. Normalization with transient relapse = 5 (11.9%); IV. Temporary normalization and relapse during IFN = 4 (9.1%); V. Partial response (more than 50% of ALT decrease) = 7 (15.9%); VI. No response = 12 (27.3%). In conclusion, ALT patterns vary widely during and after IFN treatment and can be classified in at least 6 types.

Autoantibodies before, during and after administration of recombinant interferon-alpha for chronic viral hepatitis

This study was undertaken to investigate the presence of autoantibodies in patients with chronic viral hepatitis B and C, before, during and after interferon-alpha (IFN-alpha) therapy and to study their relation to dose and type of IFN-alpha and response to treatment. Fifty patients with chronic hepatitis were divided in two groups, a control-group of 21 patients (10 type B and 11 type C) who were followed for 6 months without treatment and an IFN-group consisting of 29 patients (8 type B and 21 type C) who received IFN therapy for 6 months. Serum samples were tested for a range of antibodies at the start of the study, during therapy and at the end of the 6 month period. Antibodies tested for included: antinuclear, smooth muscle, antimitochondrial, parietal cell and thyroid microsomal. Four (8%) of the total patient group had autoantibodies at the beginning of the study (two in each group). During the follow-up period no patient in the control group developed antibodies compared with 3 (11%) patients in the treatment group. Autoantibodies developed in patients treated with higher doses of IFN and were found in those patients who tended to show a poor response to IFN-therapy. Further studies are needed to establish the relationship between poor response to IFN-alpha and development of autoantibodies.

Plasma levels of tumor necrosis factor-alpha in patients with visceral leishmaniasis (Kala-Azar). Association with activity of the disease and clinical remisson following antimonial therapy

Evaluation of TNF-alpha in patients with Kala-azar has drawn increasing interest due to its regulatory role on the immune system, in addition to its cachetizing activity. The objective of this study was to examine the association between plasma levels of TNF-alpha, measured by immunore-activity (ELISA) and bioactivity (cytotoxicity assay with L-929 cells), and clinical manifestations of visceral leishmaniasis. Plasma samples from 19 patients with Kala-azar were obtained before, during and at the end of antimonial therapy. TNF-alpha determinations was done by using the cytotoxicity assay (all patients) and the enzyme-linked immunoassay (ELISA - 14 patients). A discrepancy between results obtained by ELISA and cytotoxicity assay was observed. Levels of circulating TNF-alpha, assessed by ELISA, were higher in patients than in healthy controls, and declined significantly with improvement in clinical and laboratory parameters. Plasma levels before treatment were 124.7 ± 93.3 pg/ml (mean ± SD) and were higher than at the end of therapy 13.9 ± 25.1 pg/ml (mean ± SD) (p = 0.001). In contrast, plasma levels of TNF-alpha evaluated by cytotoxicity assay did not follow a predicted course during follow-up. Lysis, in this case, might be not totally attributed to TNF-alpha. The discrepancy might be attributed to the presence of factor(s) known to influence the release and activity of TNF-alpha.

Multi-objective Parallel Particle Swarm Optimization for Day-ahead Vehicle-To-Grid Scheduling

This paper presents a methodology for multi-objective day-ahead energy resource scheduling for smart grids considering intensive use of distributed generation and Vehicle- To-Grid (V2G). The main focus is the application of weighted Pareto to a multi-objective parallel particle swarm approach aiming to solve the dual-objective V2G scheduling: minimizing total operation costs and maximizing V2G income. A realistic mathematical formulation, considering the network constraints and V2G charging and discharging efficiencies is presented and parallel computing is applied to the Pareto weights. AC power flow calculation is included in the metaheuristics approach to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method.

Dispatch of distributed Energy Resources to Provide Energy and Reserve in Smart Grids using a Particle Swarm Optimization Approach

The smart grid concept is a key issue in the future power systems, namely at the distribution level, with deep concerns in the operation and planning of these systems. Several advantages and benefits for both technical and economic operation of the power system and of the electricity markets are recognized. The increasing integration of demand response and distributed generation resources, all of them mostly with small scale distributed characteristics, leads to the need of aggregating entities such as Virtual Power Players. The operation business models become more complex in the context of smart grid operation. Computational intelligence methods can be used to give a suitable solution for the resources scheduling problem considering the time constraints. This paper proposes a methodology for a joint dispatch of demand response and distributed generation to provide energy and reserve by a virtual power player that operates a distribution network. The optimal schedule minimizes the operation costs and it is obtained using a particle swarm optimization approach, which is compared with a deterministic approach used as reference methodology. The proposed method is applied to a 33-bus distribution network with 32 medium voltage consumers and 66 distributed generation units.

Decision support tool for Virtual Power Players: Hybrid Particle Swarm Optimization applied to Day-ahead Vehicle-To-Grid Scheduling

This paper presents a decision support tool methodology to help virtual power players (VPPs) in the Smart Grid (SGs) context to solve the day-ahead energy resource scheduling considering the intensive use of Distributed Generation (DG) and Vehicle-To-Grid (V2G). The main focus is the application of a new hybrid method combing a particle swarm approach and a deterministic technique based on mixedinteger linear programming (MILP) to solve the day-ahead scheduling minimizing total operation costs from the aggregator point of view. A realistic mathematical formulation, considering the electric network constraints and V2G charging and discharging efficiencies is presented. Full AC power flow calculation is included in the hybrid method to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method.

Particle Swarm Optimization of Electricity Market Negotiating Players Portfolio

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors’ research group has developed a multi-agent system: MASCEM (Multi- Agent System for Competitive Electricity Markets), which performs realistic simulations of the electricity markets. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from each market context. However, it is still necessary to adequately optimize the players’ portfolio investment. For this purpose, this paper proposes a market portfolio optimization method, based on particle swarm optimization, which provides the best investment profile for a market player, considering different market opportunities (bilateral negotiation, market sessions, and operation in different markets) and the negotiation context such as the peak and off-peak periods of the day, the type of day (business day, weekend, holiday, etc.) and most important, the renewable based distributed generation forecast. The proposed approach is tested and validated using real electricity markets data from the Iberian operator – MIBEL.

Application-Specific Modified Particle Swarm Optimization for energy resource scheduling considering vehicle-to-grid

This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding the management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem. The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intelligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence characteristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered. The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Programming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value.

Electricity Markets Portfolio Optimization using a Particle Swarm Approach

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors’ research group has developed a multi-agent system: MASCEM (Multi-Agent System for Competitive Electricity Markets), which simulates the electricity markets. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from the market context. However, it is still necessary to adequately optimize the player’s portfolio investment. For this purpose, this paper proposes a market portfolio optimization method, based on particle swarm optimization, which provides the best investment profile for a market player, considering the different markets the player is acting on in each moment, and depending on different contexts of negotiation, such as the peak and offpeak periods of the day, and the type of day (business day, weekend, holiday, etc.). The proposed approach is tested and validated using real electricity markets data from the Iberian operator – OMIE.

Quantum-based Particle Swarm Optimization Application to Studies of Aggregated Consumption Shifting and Generation Scheduling in Smart Grids

Demand response programs and models have been developed and implemented for an improved performance of electricity markets, taking full advantage of smart grids. Studying and addressing the consumers’ flexibility and network operation scenarios makes possible to design improved demand response models and programs. The methodology proposed in the present paper aims to address the definition of demand response programs that consider the demand shifting between periods, regarding the occurrence of multi-period demand response events. The optimization model focuses on minimizing the network and resources operation costs for a Virtual Power Player. Quantum Particle Swarm Optimization has been used in order to obtain the solutions for the optimization model that is applied to a large set of operation scenarios. The implemented case study illustrates the use of the proposed methodology to support the decisions of the Virtual Power Player in what concerns the duration of each demand response event.

Chronic hepatitis C virus infections in Brazilian patients: association with genotypes, clinical parameters and response to long term alpha interferon therapy

The present study assessed the clinical significance of hepatitis C virus (HCV) genotypes and their influence on response to long term recombinant-interferon-alpha (r-IFN-a) therapy in Brazilian patients. One hundred and thirty samples from patients previously genotyped for the HCV and with histologically confirmed chronic hepatitis C (CH-C) were evaluated for clinical and epidemiological parameters (sex, age, time of HCV infection and transmission routes). No difference in disease activity, sex, age or mode and time of transmission were seen among patients infected with HCV types 1, 2 or 3. One hundred and thirteen of them were treated with 3 million units of r-IFN-a, 3 times a week for 12 months. Initial response (IR) was significantly better in patients with genotype 2 (100%) and 3 (46%) infections than in patients with genotype 1 (29%) (p < 0.005). Among subtypes, difference in IR was observed between 1b and 2 (p < 0.005), and between 1b and 3a (p < 0.05). Sustained response (SR) was observed in 12% for (sub)type 1a, 13% for 1b, 19% for 3a, and 40% for type 2; significant differences were found between 1b and 2 (p < 0.001), and between 1b and 3a (p < 0.05). Moreover, presence of cirrhosis was significantly associated with non response and response with relapse (p < 0.05). In conclusion, non-1 HCV genotype and lack of histological diagnosis of cirrhosis were the only baseline features associated with sustained response to treatment. These data indicate that HCV genotyping may have prognostic relevance in the responsiveness to r-IFN-a therapy in Brazilian patients with chronic HCV infection, as seen in other reports worldwide.