Cure kinetics and thermodynamics of polyurethane network formation based on castor oil based polyester polyol and 4,4’-diphenyl methane diisocyanate

336-342In this work, isothermal curing kinetics of a non-catalysed and non-blown reaction between castor oil based polyester polyol and polymeric 4, 4’-diphenyl methane diisocyanate (MDI) has been investigated using Differential Scanning calorimeter (DSC) and viscosity build up studies. Several phenomenological models like Ozawa, Kissinger and Kissinger- Akahira-Sunose (KAS) isoconversional methods has been adopted to study polymerisation kinetics through DSC. DSC cure kinetics is studied at different heating rates (5°C/min, 10°C/min, 15°C/min and 20°C/min). Viscosity build up studies are also done for evaluating the kinetic parameters. These studies have been conducted for an isocyanate index [NCO equivalents/OH equivalents] of 1:1 and 1:2. Dynamic viscosity is measured as a function of time and rate constant and activation energy of the curing system is evaluated. Activation energy obtained for 1:1 index through Ozawa and Kissinger methods is found to be in the range of 70kJ/mol and for 1:2 index it is found to be in the range of 50kJ/mol. Thermodynamic parameters like Gibb’s free energy (Activation Free Energy), activation enthalpy and activation entropy of the polymerisation kinetics is calculated using Wynne-Jones-Eyring-Evans Theory

Optimizing investments in cyber hygiene for protecting healthcare users

Cyber hygiene measures are often recommended for strengthening an organization’s security posture, especially for protecting against social engineering attacks that target the human element. However, the related recommendations are typically the same for all organizations and their employees, regardless of the nature and the level of risk for different groups of users. Building upon an existing cybersecurity investment model, this paper presents a tool for optimal selection of cyber hygiene safeguards, which we refer as the Optimal Safeguards Tool (OST). The model combines game theory and combinatorial optimization (0-1 Knapsack) taking into account the probability of each user group to being attacked, the value of assets accessible by each group, and the efficacy of each control for a particular group. The model considers indirect cost as the time employees could require for learning and trainning against an implemented control. Utilizing a game-theoretic framework to support the Knapsack optimization problem permits us to optimally select safeguards’ application levels minimizing the aggregated expected damage within a security investment budget. We evaluate OST in a healthcare domain use case. In particular, on the Critical Internet Security (CIS) Control group 17 for implementing security awareness and training programs for employees belonging to the ICT, clinical and administration personnel of a hospital. We compare the strategies implemented by OST against alternative common-sense defending approaches for three different types of attackers: Nash, Weighted and Opportunistic. Our results show that Nash defending strategies are consistently better than the competing strategies for all attacker types with a minor exception where the Nash defending strategy, for a specific game, performs at least as good as other common-sense approaches. Finally, we illustrate the alternative investment strategies on different Nash equilibria (called plans) and discuss the optimal choice using the framework of 0-1 Knapsack optimization

Family-Based Model Checking with mCRL2

\u3cp\u3eFamily-based model checking targets the simultaneous verfication of multiple system variants, a technique to handle feature-based variability that is intrinsic to software product lines (SPLs). We present an approach for family-based verification based on the feature μ-calculus μL\u3csub\u3ef\u3c/sub\u3e, which combines modalities with feature expressions. This logic is interpreted over featured transition systems, a well-accepted model of SPLs, which allows one to reason over the collective behavior of a number of variants (a family of products). Via an embedding into the modal μ-calculus with data, underpinned by the general-purpose mCRL2 toolset, off-the-shelf tool support for μLf becomes readily available. We illustrate the feasibility of our approach on an SPL benchmark model and show the runtime improvement that family-based model checking with mCRL2 offers with respect to model checking the benchmark product-by-product.\u3c/p\u3

Selected challenges of software evolution for automated production systems

Automated machines and plants are operated for some decades and undergo an everlasting evolution during this time. In this paper, we present three related open evolution challenges focusing on software evolution in the domain of automated production systems, i.e. evolution and co-evolution of (interdisciplinary) engineering models and code, quality assurance as well as variant and version management during evolution

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