Abstract
The expected growth of Distributed Generation (DG) penetration in distribution systems will fundamentally alter both planning and operating procedures of Distribution Network Operators (DNO). This means that distribution networks can no longer be considered as a passive appendage to the transmission network and should be explored actively to take full advantage of the capabilities of DG units available and avoid technical problems (such as line overloading or poor voltage profiles) resulting from massive integration of this type of sources. Presently, when the capacity of the generation, transmission and distribution systems is exceeded, the traditional utility response is expanding or reinforcing existing circuits through large investments in power transformers, substations or distribution feeders. However, in some situations such as in congested metropolitan areas these actions can have prohibitive costs or simply be impossible due to space restrictions, for instance. Although current investment costs of many solutions for energy storage remain extremely high, recent developments and advances in both energy storage technologies and power electronic interfaces are opening new doors to the inclusion of Energy Storage Systems (ESS) as a potentially viable solution for modern power applications, including their use in distribution network planning and operation. This paper presents a heuristic approach for siting and sizing of ESS in distribution networks in order to maximize the capacity of DG that can be integrated in the grid without bringing technical problems to network operation. The proposed methodology enables a technical and economical comparison between a strategy based on ESS deployment and exploitation and typical traditional DNO grid reinforcement strategies. Several technologies for ESS were considered, each one with different costs and technical characteristics. The proposed methodology was validated using a real Portuguese Medium Voltage (MV) distribution network.

Abstract
The last decade has been characterized by times of change, including increasing levels of globalization and competition. Collaboration allowed SMEs to respond effectively to the development of new complex products and services but presented new barriers concerning the management of their business processes and relations with partners. This paper presents a new collaborative aggregate planning for non-hierarchical business networks, where SMEs collaborate in the selection of partners and definition of the operations plan required to specify, manufacture and distribute a custom made products. The proposed approach is based on negotiations undertaken by the partners in order to promote the creation of alternative operations plans and allow a multicriteria evaluation of the involved costs and delivery dates. The proposed approach promotes the reduction of communication between partners, a reactive analysis of changes to planning and associated impact, enhancement of trust (since all partners have the same weight on the decision process).

Abstract
This work proposes different kind of solid-contact graphite-based electrodes for the selective determination of sulphonamides (SPHs) in pharmaceuticals, biological fluids and aquaculture waters. Sulfadiazine (SDZ) and sulfamethoxazole (SMX) were selected for this purpose for being the most representative compounds of this group. The template molecules were imprinted in sol-gel (ISG) and the resulting material was used as detecting element. This was made by employing it as either a sensing layer or an ionophore of PVC-based membranes and subsequent potentiometric transduction, a strategy never reported before. The corresponding non-imprinted sol-gel (NISG) membranes were used as blank. The effect of plasticizer and kind/charge of ionic lipophilic additive was also studied. The best performance in terms of slope, linearity ranges and signal reproducibility and repeatability was achieved by PVC membranes including a high dielectric constant plasticizer and 15 mg of ISG particles. The corresponding average slope was -51.4 and -52.4 mV/decade, linear responses were 9.0 x 10(-6) and 1.7x10(-5) M, and limits of detection were 0.74 and 1.3 mu g/mL for SDZ and for SMX, respectively. Good selectivity with log Kpot< -0.3 was observed for carbonate, chloride, fluoride, hydrogenocarbonate, nitrate, nitrite, phosphate, cyanide, sulfate, borate, persulphate, citrate, tartrate, salicylate, tetracycline, ciprofloxacin, sulphamerazine, sulphatiazole, dopamine, glucose, galactose, cysteine and creatinine. The best sensors were successfully applied to the analysis of real samples with relative errors ranging from -6.8 to +3.7%.

Abstract
In this paper, economic analysis of gas-fired generator, biomass unit and wind turbine is carried out in different scenarios based on gas price, energy selling price, and load factor in Iran and the payback period and rate of return are determined. Then, the participation of the Distributed Generations (DGs) in a day-ahead energy and reserve market is simulated for scheduling from the view point of ISO. The ISO target in this model is minimizing total system producing cost considering system security constraints and technical limitation of all units. Finally, the simulation results demonstrate the reserve market as an apt place for DGs.

Abstract
This paper develops a theoretical framework to study spatial price competition in a Hotelling-type network game. Each firm i is represented by a node of degree k(i), where k(i) is the number of firm i 's direct competitors (neighbors). We investigate price competition a la Hotelling with complete and incomplete information about the network structure. The goal is to investigate the effects of the network structure and of the uncertainty on firms' prices and profits. We first analyze the benchmark case where each firm knows its own degree as well as the rivals' degree. Then, in order to understand the role of information in the price competition network, we also analyze the incomplete information case where each firm knows its type (i.e. number of connections) but not the competitors' type.

Abstract
As in batch learning, one may identify a class of streaming real-world problems which require the modeling of several targets simultaneously. Due to the dependencies among the targets, simultaneous modeling can be more successful and informative than creating independent models for each target. As a result one may obtain a smaller model able to simultaneously explain the relations between the input attributes and the targets. This problem has not been addressed previously in the streaming setting. We propose an algorithm for inducing multi-target model trees with low computational complexity, based on the principles of predictive clustering trees and probability bounds for supporting splitting decisions. Linear models are computed for each target separately, by incremental training of perceptrons in the leaves of the tree. Experiments are performed on synthetic and real-world datasets. The multi-target regression tree algorithm produces equally accurate and smaller models for simultaneous prediction of all the target attributes, as compared to a set of independent regression trees built separately for each target attribute. When the regression surface is smooth, the linear models computed in the leaves significantly improve the accuracy for all of the targets. © 2011 ACM.