An evolutionary real options framework for the design and management of projects and systems with complex real options and exercising conditions

To address the issue of decision support for designing and managing flexible projects and systems in the face of uncertainties, this paper integrates real options valuation, decision analysis techniques, Monte Carlo simulations and evolutionary algorithms in an evolutionary real options framework. The proposed evolutionary real options framework searches for an optimized portfolio of real options and makes adaptive plans to cope with uncertainties as the future unfolds. Exemplified through a test case, the evolutionary framework not only compares favorably with traditional fixed design approaches but also delivers considerable improvements over prevailing real options practices.     

A GUI for Jess

The paper describes JessGUI, a graphical user interface developed on top of the Jess expert system shell. The central idea of the JessGUI project was to make building, revising, updating, and testing Jess-based expert systems easier, more flexible, and more user friendly. There are many other expert system building tools providing a rich and comfortable integrated development environment to expert system builders. However, they are all either commercial or proprietary products. Jess and JessGUI are open-source freeware, and yet they are well suited for building even complex expert system applications, both stand-alone and Web-based ones. An important feature of JessGUI is its capability of saving knowledge bases in XML format (in addition to the original Jess format), thus making them potentially easy to interoperate with other knowledge bases on the Internet. Jess and JessGUI are also used as practical knowledge engineering tools to support both introductory and advanced university courses on expert systems. The paper presents design details of JessGUI, explains its links with the underlying Jess knowledge representation and reasoning tools, and shows examples of using JessGUI in expert system development. It also discusses some of the current efforts in extending Jess/JessGUI in order to provide intelligent features originally not supported in Jess.     

Regulation of S100As Expression by Inflammatory Cytokines in Chronic Lymphocytic Leukemia

The calcium-binding proteins S100A4, S100A8, and S100A9 are upregulated in chronic lymphocytic leukemia (CLL), while the S100A9 promotes NF-κB activity during disease progression. The S100-protein family has been involved in several malignancies as mediators of inflammation and proliferation. The hypothesis of our study is that S100A proteins are mediators in signaling pathways associated with inflammation-induced proliferation, such as NF-κB, PI3K/AKT, and JAK/STAT. The mononuclear cells (MNCs) of CLL were treated with proinflammatory IL-6, anti-inflammatory IL-10 cytokines, inhibitors of JAK1/2, NF-κB, and PI3K signaling pathways, to evaluate S100A4, S100A8, S100A9, and S100A12 expression as well as NF-κB activation by qRT-PCR, immunocytochemistry, and immunoblotting. The quantity of S100A4, S100A8, and S100A9 positive cells (p < 0.05) and their protein expression (p < 0.01) were significantly decreased in MNCs of CLL patients compared to healthy controls. The S100A levels were generally increased in CD19⁺ cells compared to MNCs of CLL. The S100A4 gene expression was significantly stimulated (p < 0.05) by the inhibition of the PI3K/AKT signaling pathway in MNCs. IL-6 stimulated S100A4 and S100A8 protein expression, prevented by the NF-κB and JAK1/2 inhibitors. In contrast, IL-10 reduced S100A8, S100A9, and S100A12 protein expressions in MNCs of CLL. Moreover, IL-10 inhibited activation of NF-κB signaling (4-fold, p < 0.05). In conclusion, inflammation stimulated the S100A protein expression mediated via the proliferation-related signaling and balanced by the cytokines in CLL.     

Guest editorial for EMMSAD’2020 special section

Software and Systems Modeling -     

Determination of 200 °C Isothermal Section of Al-Ag-Ga Phase Diagram by Microanalysis,X-ray Diffraction,Hardness and Electrical Conductivity Measurements

Ternary Al-Ag-Ga system at 200 °C was experimentally and thermodynamically assessed. Isothermal section was extrapolated using optimized thermodynamic parameters for constitutive binary systems. Microstructure and phase composition of the selected alloy samples were analyzed using light microscopy, scanning electron microscopy combined with energy-dispersive spectrometry and x-ray powder diffraction technique. The obtained experimental results were found to be in a close agreement with the predicted phase equilibria. Hardness and electrical conductivity of the alloy samples from four vertical sections Al-Ag₈₀Ga₂₀, Al-Ag₆₀Ga₄₀, Ag-Al₈₀Ga₂₀ and Ag-Al₆₀Ga₄₀ of the ternary Al-Ag-Ga system at 200 °C were experimentally determined using Brinell method and eddy current measurements. Additionally, hardness of the individual phases present in the microstructure of the studied alloy samples was determined using Vickers microhardness test. Based on experimentally obtained results, isolines of Brinell hardness and electrical conductivity were calculated for the alloys from isothermal section of the ternary Al-Ag-Ga system at 200 °C.     

Declarative and Preferential Bias in GP-based Scientific Discovery

This work examines two methods for evolving dimensionally correct equations on the basis of data. It is demonstrated that the use of units of measurement aids in evolving equations that are amenable to interpretation by domain specialists. One method uses a strong typing approach that implements a declarative bias towards correct equations, the other method uses a coercion mechanism in order to implement a preferential bias towards the same objective. Four experiments using real-world, unsolved scientific problems were performed in order to examine the differences between the approaches and to judge the worth of the induction methods.Not only does the coercion approach perform significantly better on two out of the four problems when compared to the strongly typed approach, but it also regularizes the expressions it induces, resulting in a more reliable search process.A trade-off between type correctness and ability to solve the problem is identified. Due to the preferential bias implemented in the coercion approach, this trade-off does not lead to sub-optimal performance. No evidence is found that the reduction of the search space achieved through declarative bias helps in finding better solutions faster. In fact, for the class of scientific discovery problems the opposite seems to be the case.