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We propose a new method to estimate benefits of road network improvements, which allows to include the induced demand without arbitrary assumptions. Instead of estimating induced demand (which is nontrivial and hardly possible in practice), we search for demand induction where initial benefits are mitigated to zero. Such approach allows to formulate a dual measure of benefit, covering both the potential benefits and the likelihood of consuming them by the induced traffic. We first estimate benefits of road network improvement assuming that traffic demand is fixed. Consequently, we find demand model configurations at which the benefits of the new investment become null, i.e., all the initial benefits are consumed by the traffic demand growth. We call such states of induced demand the Lewis–Mogridge points of the analysed improvement. We select the most probable of such points and use it to calculate the proposed novel indicator μ, for which the initial benefits (obtained under a fixed-demand assumption) are multiplied with a demand increase rate needed to consume them. We believe that such measure allows to include the critical phenomena of induced traffic and, at the same time, to overcome problems associated with reliable estimation of induced demand. As we illustrate with the case of two alternative road improvement schemes in Kraków, Poland, the proposed method allows to estimate maximal threshold of induced traffic and to select scenario more resilient to induced traffic.

Sodium iron hexacyanoferrate (NaFeHCF) films were electrodeposited on Au/Cr/Si for the study of growth behavior and physical properties. The NaFeHCF films were studied by different analytical methods to prove the chemical composition, morphology and crystal structure. The grains of the film grow with a cubic structure with an average lattice parameter of 10.10 Å and the preferential growth direction along the [111] direction of the cubic cell. The films show a repeatable bipolar resistive switching behavior accompanied by high current changes (up to a factor of ~105). The different resistive states in the materials are dominated by ohmic conduction.

Prussian blue (PB) layers were electrodeposited for the fabrication of Au/PB/Ag stacks to study the resistive switching effect. The PB layers were characterized by different techniques to prove the homogeneity, composition, and structure. Electrical measurements confirmed the bipolar switching behavior with at least 3 orders of magnitude in current and the effect persisting for the 200 cycles tested. The low resistance state follows the ohmic conduction with an activation energy of 0.2 eV.

The book is the third of four volumes on data science and artificial intelligence. This third volume covers fundamentals of unsupervised learning including clustering techniques, simple generative models, anomaly detection and nonlinear dimensionality reduction.

The book 'Reinforcement Learning: An Introduction' by Sutton and Barto is the standard text book for introductory courses to reinforcement learning. Next to concrete algorithms and extensive examples the book contains several fundamental results related to Markov decision processes (MDPs) and Bellman equations in Chapters 3 and 4. Unfortunately some proofs are missing, some theorems lack precise formulation, and for some results the line of arguments is quite garbled. In this note we provide all missing proofs, give precise formulations of theorems and untangle the line of arguments. Further, we avoid using random variables and their expected values. Since we (like Sutton/Barto) restrict our attention to finite MDPs all expected values can be made explicit avoiding overloaded notation and murky conclusions. This article bridges the gap between introductory literature like Sutton/Barto and research literature containing exact formulations and proofs of relevant results, but being less accessible to beginners due to higher generality and complexity.