Objective and subjective evaluation of power plants and their non-radioactive emissions using the analytic hierarchy process

Non-nuclear power plant emissions are of great concern to the public and to scientists alike. As energy demand tends to rise rapidly, especially in the developing countries, the negative effects to human health and to the environment from gaseous emissions together with hazardous particulate matter released by power plants can no longer be ignored.     

Technological,economic and sustainability evaluation of power plants using the Analytic Hierarchy Process

Complexity of power plant evaluation is steadily rising, as more criteria are involved in the overall assessment while evaluation data change rapidly. Apart from evaluating several aspects of power plants separately, a multicriteria analysis based on hierarchically structured criteria is necessary, so as to address the overall assessment of power plants according to the technological, economic and sustainability aspects. For this reason, in this paper, ten types of power plant are evaluated using nine end node criteria properly structured under the Analytical Hierarchy Process. Moreover, pairwise comparisons allow for accurate subjective criteria weighting.     

Robust discrete-time control design with time-varying parametric uncertainties

A novel multiplier approach for robust controller design in discrete-time systems with real, time-varying parametric uncertainty is presented. An important feature of our approach is that bounds on the rate of variation of the uncertain parameters are assumed and, unlike in most related approaches, dynamic multipliers are obtained that utilize this information. A convex minimization procedure formulated as an LMI problem is presented to obtain multipliers that satisfy the robustness conditions derived. Such conditions are transformed to an equivalent scaled H_∞ norm condition and a μ/k_m-synthesis approach is proposed to design robust controllers. Copyright © 1999 John Wiley & Sons, Ltd.     

mTOR Signaling Components in Tumor Mechanobiology

Mechanistic target of rapamycin (mTOR) is a central signaling hub that integrates networks of nutrient availability, cellular metabolism, and autophagy in eukaryotic cells. mTOR kinase, along with its upstream regulators and downstream substrates, is upregulated in most human malignancies. At the same time, mechanical forces from the tumor microenvironment and mechanotransduction promote cancer cells’ proliferation, motility, and invasion. mTOR signaling pathway has been recently found on the crossroads of mechanoresponsive-induced signaling cascades to regulate cell growth, invasion, and metastasis in cancer cells. In this review, we examine the emerging association of mTOR signaling components with certain protein tools of tumor mechanobiology. Thereby, we highlight novel mechanisms of mechanotransduction, which regulate tumor progression and invasion, as well as mechanisms related to the therapeutic efficacy of antitumor drugs.     

Toughening and Healing of CFRPs by Diels–Alder-Based Nano-Modified Resin through Melt Electro-Writing Process Technique

In the current study, a novel approach in terms of the incorporation of self-healing agent (SHA) into unidirectional (UD) carbon fiber reinforced plastics (CFRPs) has been demonstrated. More precisely, Diels–Alder (DA) mechanism-based resin (Bis-maleimide type) containing or not four layered graphene nanoplatelets (GNPs) at the amount of 1 wt% was integrated locally in the mid-thickness area of CFRPs by melt electro-writing process (MEP). Based on that, CFRPs containing or not SHA were fabricated and further tested under Mode I interlaminar fracture toughness experiments. According to experimental results, modified CFRPs exhibited a considerable enhancement in the interlaminar fracture toughness properties (peak load (P_max) and fracture toughness energy I (G_IC) values). After Mode I interlaminar fracture toughness testing, the damaged samples followed the healing process and then were tested again under identical experimental conditions. The repeating of the tests revealed moderate healing efficiency (H.E.) since part of the interlaminar fracture toughness properties were restored. Furthermore, three-point bending (3PB) experiments were conducted, with the aim of assessing the effect of the incorporated SHA on the in-plane mechanical properties of the final CFRPs. Finally, optical microscopy (OM) examinations were performed to investigate the activated/involved damage mechanisms.     

Evolution of aggregate size and fractal dimension during Brownian coagulation

Fractal aggregate coagulation is described within a general framework of multivariate population dynamics. The effect of aggregate morphology on the coagulation rate, is taken into account explicitly, introducing in addition to aggregate particle size, the aggregate fractal dimension, as a second independent variable. A simple constitutive law is derived for determining the fractal dimension of an aggregate, resulting from a coagulation event between aggregates with different fractal dimensions. An efficient Monte Carlo method was implemented to solve the resulting bivariate Brownian coagulation equation, in the limits of continuum and free molecular flow regimes. The results indicate that as the population mean fractal dimension goes from its initial value towards its asymptotic value, the distribution of fractal dimension remains narrow for both flow regimes. The evolution of the mean aggregate size in the continuum regime is found to be nearly independent of aggregate morphology. In the free molecular regime however, the effects of aggregate morphology, as embodied in its fractal dimension, become more important. In this case the evolution of the aggregate size distribution cannot be described by the traditional approach, that employs a constant fractal dimension.     

On the rain attenuation dynamics: spatial‐temporal analysis of rainfall rate and fade duration statistics

Knowledge about the dynamic characteristics of rain attenuation is of utmost importance for many applications in terrestrial and satellite communication systems operating at frequencies above 10 GHz. Long‐term rain rate statistics and rain rate duration statistics are usually available from meteorological data. In this paper, a spatial–temporal analysis is employed in order to evaluate the rain attenuation power spectrum of a terrestrial/satellite path. The predicted power spectrum is compared with experimental data. Based on the spectral analysis of rainfall rate a method for converting rain rate duration statistics to link fade duration statistics is also proposed. Fade duration statistics are presented for terrestrial and satellite links and compared with available experimental data. The agreement between the predicted results and the experimental data has been found to be quite encouraging. Finally, numerical results are presented for various climatic zones, elevation angles and frequencies. Some very useful conclusions concerning the dynamic properties of rain attenuation for a microwave path are deduced. Copyright © 2003 John Wiley & Sons, Ltd.     

Comparing the inference capabilities of binary, trivalent and sigmoid fuzzy cognitive maps

In this paper, we compare the inference capabilities of three different types of fuzzy cognitive maps (FCMs). A fuzzy cognitive map is a recurrent artificial neural network that creates models as collections of concepts/neurons and the various causal relations that exist between these concepts/neurons. In the paper, a variety of industry/engineering FCM applications is presented. The three different types of FCMs that we study and compare are the binary, the trivalent and the sigmoid FCM, each of them using the corresponding transfer function for their neurons/concepts. Predictions are made by viewing dynamically the consequences of the various imposed scenarios. The prediction making capabilities are examined and presented. Conclusions are drawn concerning the use of the three types of FCMs for making predictions. Guidance is given, in order FCM users to choose the most suitable type of FCM, according to (a) the nature of the problem, (b) the required representation capabilities of the problem and (c) the level of inference required by the case.     

Stochastic interest rates in the analysis of energy investments: Implications on economic performance and sustainability

A systematic impact assessment of stochastic interest and inflation rates on the analysis of energy investments is presented. A real-options algorithm has been created for this task. Constant interest rates incorporating high risk premium have been extensively used for economic calculations, within the framework of traditional direct cash flow methods, thus favouring immediate, irreversible investments in the expense of, sometimes, insubstantially low anticipated yields. In this article, not only incomes and expenses but also interest and inflation rates are considered stochastically evolving according to specific probabilistic models. The numerical experiments indicated that the stochastic interest rate forecasts fluctuate in such low levels that may signal delayed investment entry in favour of higher expected yields. The implementation of stochastically evolving interest rates in energy investment analysis may have a controversial effect on sustainability. Displacements of inefficient plants may be significantly delayed, thus prolonging high CO₂ emission rates. Under the current CO₂ allowance prices or their medium-term forecasts, this situation may not be improved and flexible policy interventions may be necessitated.