This paper presents a methodology to constrain the optimization problem in LV-MPC so that validity of predictions can be ascertained. LV-MPC is a model-based predictive control methodology implemented in the space of the latent variables and is based on a linear predictor. Provided real processes are non-linear, there is model-process mismatch, and under tight control, the predictor can be used for extrapolation. Extrapolation leads to bad predictions which deteriorates control performance, hence the interest in validity of predictions. In the proposed approach first two validity indicators on predictions are defined. The novelty in the two indicators proposed is they neglect past data, and so validity of predictions is ascertained in terms of future moves which are actually the degrees of freedom in the optimization. Second, the indicators are introduced in the optimization as constraints. Provided the indicators are quadratic, recursive optimization with linearised constraints is implemented. A MIMO example shows how ensuring validity of predictions neglecting past data can improve closed-loop performance, specially under tight control outside the identification region.

Despite the widespread use of the classical bicriteria Markowitz mean-variance framework, a broad consensus is emerging on the need to include more criteria for complex portfolio selection problems. Sustainable investing, also called socially responsible investment, is becoming a mainstream investment practice. In recent years, some scholars have attempted to include sustainability as a third criterion to better reflect the individual preferences of those ethical or green investors who are willing to combine strong financial performance with social benefits. For this purpose, new computational methods for optimizing this complex multiobjective problem are needed. Multiobjective evolutionary algorithms (MOEAs) have been recently used for portfolio selection, thus extending the mean-variance methodology to obtain a mean-variance-sustainability nondominated surface. In this paper, we apply a recent multiobjective genetic algorithm based on the concept of ε-dominance called ev-MOGA. This algorithm tries to ensure convergence towards the Pareto set in a smart distributed manner with limited memory resources. It also adjusts the limits of the Pareto front dynamically and prevents solutions belonging to the ends of the front from being lost. Moreover, the individual preferences of socially responsible investors could be visualised using a novel tool, known as level diagrams, which helps investors better understand the range of values attainable and the tradeoff between return, risk, and sustainability.

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To transmit the teaching of digital electronics today, the considerations outlined below must be kept in mind. Digital electronics is evolving quickly and its techniques and tools have revolutionized the manner of analyzing, simulating, synthesizing and veriJLing digital systems. Microelectronic development is more and more depending on the technology and on design methodology. Standard hardware description languages (Verilog and VHDL) together with simulation and synthesis tools are some of the drivers behind microelectronic development.The "constructivist '' model in the teaching-learning process proposes: significant learning as opposed to memorizing; structuring and sequencing of content; learning through guided discovery and a spiral or recurring procedure.These circumstances together with pedagogical concern and the knowledge and experience that the authors have acquired as digital electronics teaching Professors have resulted in the formulation of a curricular proposal to transmit this updated teaching method. Using this teaching model as a base, the aim of this proposal is to have the student studying digital electronics acquire the theory and practices (know-how) from the beginning by using current design methodologies and CAD and EDA tools that the students will use in their professional Jirture. Another objective of this method is to provide students with multimedia applications as a learning resource.Index Terms -computer aided electronic engineering, simulation and tools, digital electronics analysis and design.

CP is a deterministic model like WGP in this aspect. Therefore, CP seems inappropriate to select stock portfolios from the Eu(R) maximization theory. In contrast to MV-SGP model, CP does not generalize Markowitz M-V model to multiple objectives. This lack of strictness is mitigated by the linkage between CP and utility theory established in Chap. 8. This linkage allows us to extend utility properties to CP approaches. We show the CP setting for portfolio selection by establishing and graphing its main elements: profitability-safety efficient frontier, ideal point and the bounds of Yu compromise set, which is the landing area on which the profitability-safety utility function reaches its maximum. From these variables, expected return and safety, the portfolio selection problem is defined in terms of CP.