Abstract
Industrial processes use energy to transform raw materials and intermediate goods into final products. Many efforts have been done on the minimization of energy costs in industrial plants. Apart from working on "how" an industrial process is implemented, it is possible to reduce the energy costs by focusing on "when" it is performed. Although, some manufacturing plants (e.g. refining or petrochemical plants) can be inflexible with respect to time due to interdependencies in processes that must be respected for performance and safety reasons, there are other industrial segments, such as alumina plants or discrete manufacturing, with more degrees of flexibility. These manufacturing plants can consider a more flexible scheduling of the most energy-intensive processes in response to dynamic prices and overall condition of the electricity market. In this scenario, requests for energy can be encoded by means of a formal structure called flex-offers, then aggregated (joining several flex-offers into a bigger one) and sent to the market, scheduled, disaggregated and transformed into consumption plans, and eventually, into production schedules for given industrial plant. In this paper, we describe the flex-offer concept and how it can be applied to industrial and home automation scenarios. The architecture proposed in this paper aims to be adaptable to multiples scenarios (industrial, home and building automation, etc.), thus providing the foundations for different concept implementations using multiple technologies or supporting various kinds of devices.

Abstract
This paper presents a new computational technique in composite generation and transmission expansion planning considering reliability and cost assessment. The proposed procedure incorporates a virtual database in order to hedging the repetitive calculation by optimisation solver. Since generation and transmission expansion planning is a large scale, mixed-integer, nonlinear and non-convex optimisation task, the proposed technique accelerates the convergence time and reduces computational burden. The composite generation and transmission expansion planning problem is represented as a multi-objective optimisation problem. The virtual database-supported non-dominated sorting genetic algorithm (VDS-NSGA-II) is applied due to comparative assessment potential and good handling of the non-convex problems and non-commensurable objective functions. The virtual database eliminates the repetitive computational efforts in both reliability and hourly operational assessments. In this study, the expected energy not served at hierarchical level II is taken into account as a reliability index, whereas the entire system cost, including annually operational and investment costs, is considered as another objective function. The incidence matrix-based DC optimal power flow is adopted to reflect transmission flow constraint in a framework in which the disconnected bus problem would be handled in both objective functions. To numerically evaluate the efficiency of the proposed method, simulation results on a simple three-bus test system and the modified IEEE 24-bus reliability test system are provided. In spite of huge computation burden at HL-II reliability assessment, the results indicate high efficiency of the proposed VDS-NSGA-II. © 2014 Taylor & Francis.

Abstract

Abstract
This paper analyzes transparent two-TFT current mirrors using a-GIZO TFTs with different mirroring ratios. In order to achieve a high mirroring ratio, the output TFT in the circuit employed a fingered structure layout to minimize area and overlap capacitance. The analysis of the current mirrors is performed in three phases. In the first, a radial basis function based (RBF) model is developed using measured data from fabricated TFTs on the same chip. Then, in the second phase, the RBF model is implemented in Verilog-A that is used to simulate two-TFT current mirrors with different mirroring ratios. The simulations are carried out using Cadence spectre simulator. In the third phase, simulation results are validated with the measured response from the fabricated circuits.

Abstract
Transcriptional regulation plays an important role in every cellular decision. Unfortunately, understanding the dynamics that govern how a cell will respond to diverse environmental cues is difficult using intuition alone. We introduce logic-based regulation models based on state-of-the-art work on statistical relational learning, and validate our approach by using it to analyze time-series gene expression data of the Hog1 pathway. Our results show that plausible regulatory networks can be learned from time series gene expression data using a probabilistic logical model. Hence, network hypotheses can be generated from existing gene expression data for use by experimental biologists.

Abstract
We have developed a framework to support adaptive elements in Web pages. In particular we focus on adaptive menus. Developers are able to define rules for menu adaptation according to the features of the device and browser in use. This paper briefly describes the selected adaptation patterns and their implementation. © Springer International Publishing Switzerland 2014.