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.     

Washing when the sun is shining! How users interact with a household energy management system

To make optimal use of sustainable energy, domestic electricity consumption should shift to match local supply conditions. Energy management systems (EMS) are a new sustainable technology that can help to disrupt consumers’ habits concerning electricity consumption, whilst reinforcing desired behaviours. This research examined the factors that influence the likelihood that people will shift their electricity consumption to match sustainable supply. Twenty-one interviews were conducted with households who had used the EMS ‘Smart Wash’ for several months. The findings showed that the likelihood of behaviour change is influenced by a combination of the user's motivation, specific contextual factors and the design of the EMS. Based on these results, several recommendations are given for the future design of EMSs.

Practitioner Summary: Energy management systems (EMS) are a new technology that encourages people to shift electricity consumption to match local solar supply. Interviews among users of an EMS showed that the likelihood of behaviour change is influenced by the combination of the user's motivation, contextual factors and the EMS design.     

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.     

A fast connected components labeling algorithm and its application to real-time pupil detection

We describe a fast connected components labeling algorithm using a region coloring approach. It computes region attributes such as size, moments, and bounding boxes in a single pass through the image. Working in the context of real-time pupil detection for an eye tracking system, we compare the time performance of our algorithm with a contour tracing-based labeling approach and a region coloring method developed for a hardware eye detection system. We find that region attribute extraction performance exceeds that of these comparison methods. Further, labeling each pixel, which requires a second pass through the image, has comparable performance.     

Effect of indium on wettability of Sn–Ag–Cu solders. Experiment vs. modeling,Part I

The density and surface tension measurements of the Sn3.13Ag0.74Cu liquid alloys with 2, 3, 4, 15, 30, 50 and 75 at.% In additions were conducted in the temperature range from 431 K up to 1209 K by dilatometric technique and the maximum bubble pressure method, respectively. The results obtained in both techniques exhibited 2%–3% scattering of experimental errors, similarly to the previously investigated In–Sn and Sn–Ag–In systems. This was due to the very similar surface tension and density values of pure indium and tin. The experimental surface tensions were compared with calculated ones using data of the constituent systems, (a) by means of thermodynamic method of Butler, and (b) by the temperature and concentration relation of the surface tension. The improvement of wettability in liquid alloys containing different In additions was confirmed with a sessile drop method in the temperature interval 523 K–593 K up to 1800 s. The wetting angles decreased with temperature and increasing In concentrations in the solders from ∼37^°$\sim 37^{°}$ for the solder without In at 523 K down to ∼22^°$\sim 22^{°}$ for the solder with 75 at.% In at 593 K.     

A methodology for implementation of mobile robot in adaptive manufacturing environments

With the rapid development of technologies, many production systems and modes has been advanced with respect to manufacturing, management and information fields. The paper deals with the problem of the implementation of an autonomous industrial mobile robot in real-world industrial applications in which all these fields are considered, namely mobile robot technology, planning and scheduling and communication. A methodology for implementation consisting of: a mobile robot system design (Little Helper prototype), an appropriate industrial application (multiple-part feeding), an implementation concept for the industrial application (the Bartender Concept), a mathematical model and a genetic algorithm-based heuristic is proposed. Furthermore, in order for the mobile robot to work properly in a flexible (cloud-based) manufacturing environment, the communications and exchange of data between the mobile robot with other manufacturing systems and shop-floor operators are addressed in the methodology. The proposed methodology provides insight into how mobile robot technology and abilities contribute to cloud manufacturing systems. A real-world demonstration at an impeller production line in a factory and computational experiments are conducted to demonstrate the effectiveness of the proposed methodology.     

Linear positive control systems on time scales; controllability

Positive linear systems on arbitrary time scales are studied. The theory developed in the paper unifies and extends concepts and results known for continuous-time and discrete-time systems. A necessary and sufficient condition for a linear system on a time scale to be positive is presented. Properties of positive reachable sets are investigated and characterizations of various controllability properties are presented. A modified Gram matrix of the system is used to state necessary and sufficient condition of positive reachability of a positive system on an arbitrary time scale.