Desolvation step preceding electroreduction and the electrochemical reactions of the Ni(II)/Ni(Hg) system in dimethylformamide (DMF) and its mixtures with water

The kinetics of the electrode reaction of the Ni(II)/Ni(Hg) system in DMF and its mixtures with water at different concentrations of several background electrolytes has been studied.

Similarly to aqueous solutions, at higher background electrolyte concentrations the charge transfer step is preceded by a chemical reaction. It was deduced that this proceeding reaction is due to slow dissociation of the solvent molecule in the S¹_N process.

The change of a solvent from water to 70 vol.% of DMF decreases the standard rate constant (k_s) for about one order of magnitude. At larger DMF content k_s is virtually independent of the solvent composition. The change of k_s with the DMF content was explained by assuming the resolvation of Ni(II) ions at the electrode surface in proportion to the surface coverage of the electrode molecules of both solvents.     

Conditional entropic uncertainty relations for Tsallis entropies

The entropic uncertainty relations are a very active field of scientific inquiry. Their applications include quantum cryptography and studies of quantum phenomena such as correlations and non-locality. In this work we find entanglement-dependent entropic uncertainty relations in terms of the Tsallis entropies for states with a fixed amount of entanglement. Our main result is stated as Theorem 1. Taking the special case of von Neumann entropy and utilizing the concavity of conditional von Neumann entropies, we extend our result to mixed states. Finally we provide a lower bound on the amount of extractable key in a quantum cryptographic scenario.     

Miniaturized method for separation and quantification of nitrogen species in petroleum distillates

The properties of nitrogen compounds are a subject of interest to petroleum refiners due to the detrimental role, these compounds play in catalyst deactivation and product stability. It is thought that basic nitrogen is a major contributor to these phenomena, and therefore identification and quantification of nitrogen species by type would be of great importance.A practical miniaturized method for quantitative separation of nitrogen compounds by type in petroleum distillates is described. Solid phase extraction methodology was used to concentrate the nitrogen compounds and to separate them further into neutral and basic types. The basic nitrogen compounds could not be fully recovered, while full recovery was achieved for the neutral nitrogen types. The amount of basic nitrogen was calculated by difference, and boiling point distribution profiles were produced. For identification of nitrogen compounds in separated fractions, a combined GC-MS/AED method with retention time locking was used. Carbazole and its substituted derivatives methyl, dimethyl, and trimethylcarbazoles were identified in the product from fluid catalytic cracking.     

Electrode reactions of nickel(II) at mercury electrodes in aqueous solutions of pyridine at high ionic strengths

At high ionic strength the ion pair (NiPy²⁺₄, nX^?) or complex (NiPy₄X₂), n = 0, 1, 2; X^? = Cl^?, Br^?, SCN^?, N^?₃, F^?, NO^?₃, ClO^?₄; is adsorbed at the surface of mercury electrode. Under specified conditions in chloride, bromide, and thiocyanates solutions the electroreduction is preceded by a crystallization of a complex on the electrode surface. The inductive role of specifically coadsorbed Cl^? ions is discussed.     

Multimodal processes optimization subject to fuzzy operation time constraints: declarative modeling approach

We present an extension of the resource-constrained multi-product scheduling problem for an automated guided vehicle (AGV) served flow shop, where multiple material handling transport modes provide movement of work pieces between machining centers in the multimodal transportation network (MTN). The multimodal processes behind the multi-product production flow executed in an MTN can be seen as processes realized by using various local periodically functioning processes. The considered network of repetitively acting local transportation modes encompassing MTN’s structure provides a framework for multimodal processes scheduling treated in terms of optimization of the AGVs fleet scheduling problem subject to fuzzy operation time constraints. In the considered case, both production takt and operation execution time are described by imprecise data. The aim of the paper is to present a constraint propagation (CP) driven approach to multi-robot task allocation providing a prompt service to a set of routine queries stated in both direct and reverse way. Illustrative examples taking into account an uncertain specification of robots and workers operation time are provided.     

Milepost GCC: Machine Learning Enabled Self-tuning Compiler

Tuning compiler optimizations for rapidly evolving hardware makes porting and extending an optimizing compiler for each new platform extremely challenging. Iterative optimization is a popular approach to adapting programs to a new architecture automatically using feedback-directed compilation. However, the large number of evaluations required for each program has prevented iterative compilation from widespread take-up in production compilers. Machine learning has been proposed to tune optimizations across programs systematically but is currently limited to a few transformations, long training phases and critically lacks publicly released, stable tools. Our approach is to develop a modular, extensible, self-tuning optimization infrastructure to automatically learn the best optimizations across multiple programs and architectures based on the correlation between program features, run-time behavior and optimizations. In this paper we describe Milepost GCC, the first publicly-available open-source machine learning-based compiler. It consists of an Interactive Compilation Interface (ICI) and plugins to extract program features and exchange optimization data with the cTuning.org open public repository. It automatically adapts the internal optimization heuristic at function-level granularity to improve execution time, code size and compilation time of a new program on a given architecture. Part of the MILEPOST technology together with low-level ICI-inspired plugin framework is now included in the mainline GCC. We developed machine learning plugins based on probabilistic and transductive approaches to predict good combinations of optimizations. Our preliminary experimental results show that it is possible to automatically reduce the execution time of individual MiBench programs, some by more than a factor of 2, while also improving compilation time and code size. On average we are able to reduce the execution time of the MiBench benchmark suite by 11% for the ARC reconfigurable processor. We also present a realistic multi-objective optimization scenario for Berkeley DB library using Milepost GCC and improve execution time by approximately 17%, while reducing compilation time and code size by 12% and 7% respectively on Intel Xeon processor.     

Action rule discovery from incomplete data

Action rule is an implication rule that shows the expected change in a decision value of an object as a result of changes made to some of its conditional values. An example of an action rule is ‘credit card holders of young age are expected to keep their cards for an extended period of time if they receive a movie ticket once a year’. In this case, the decision value is the account status, and the condition value is whether the movie ticket is sent to the customer. The type of action that can be taken by the company is to send out movie tickets to young customers. The conventional action rule discovery algorithms build action rules from existing classification rules. This paper discusses an agglomerative strategy that generates the shortest action rules directly from a decision system. In particular, the algorithm can be used to discover rules from an incomplete decision system where attribute values are partially incomplete. As one of the testing domains for our research we take HEPAR system that was built through a collaboration between the Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences and physicians at the Medical Center of Postgraduate Education in Warsaw, Poland. HEPAR was designed for gathering and processing clinical data on patients with liver disorders. Action rules will be used to construct the decision-support module for HEPAR.