Redox Mediation at 11-Mercaptoundecanoic Acid Self-Assembled Monolayers on Gold

Cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and digital simulation techniques were used to investigate quantitatively the mechanism of electron transfer (ET) through densely packed and well-ordered self-assembled monolayers (SAMs) of 11-mercaptoundecanoic acid on gold, either pristine or modified by physically adsorbed glucose oxidase (GOx). In the presence of ferrocenylmethanol (FcMeOH) as a redox mediator, ET kinetics involving either solution-phase hydrophilic redox probes such as [Fe(CN)6]3-/4- or surface-immobilized GOx is greatly accelerated: [Fe(CN)6]3-/4- undergoes diffusion-controlled ET, while the enzymatic electrochemical conversion of glucose to gluconolactone is efficiently sustained by FcMeOH. Analysis of the results, also including the digital simulation of CV and EIS data, showed the prevalence of an ET mechanism according to the so-called membrane model that comprises the permeation of the redox mediator within the SAM and the intermolecular ET to the redox probe located outside the monolayer. The analysis of the catalytic current generated at the GOx/SAM electrode in the presence of glucose and FcMeOH allowed the high surface protein coverage suggested by X-ray photoelectron spectroscopy (XPS) measurements to be confirmed.

Learning Features that Predict Cue Usage

Our goal is to identify the features that predict the occurrence and placement of discourse cues in tutorial explanations in order to aid in the automatic generation of explanations. Previous attempts to devise rules for text generation were based on intuition or small numbers of constructed examples. We apply a machine learning program, C4.5, to induce decision trees for cue occurrence and placement from a corpus of data coded for a variety of features previously thought to affect cue usage. Our experiments enable us to identify the features with most predictive power, and show that machine learning can be used to induce decision trees useful for text generation.Comment: 10 pages, 2 Postscript figures, uses aclap.sty, psfig.te

A rostering approach to minimize health risks for workers: An application to a container terminal in the Italian port of Genoa

The evolving safety regulation is pushing seaports to comply with safety measures for workers performing heavy loads handling and repetitive movements. This paper proposes a risk-aware rostering approach in maritime container terminals, i.e., it addresses the rostering problem of minimizing and balancing workers’ risk in such terminals. To this end, a mixed integer mathematical programming model incorporating workforce risks is proposed, considering constraints such as the satisfaction of the workforce demand to perform the terminal operations, the worker-task compatibility and restrictions on the sequence of tasks assigned to the same worker. The model has been successfully applied to plan workforce over a six months horizon in a real container terminal located in Northern Italy, the Southern European Container Hub (SECH) in Genoa. As the workforce demand in SECH terminal is available at most two weeks in advance, a rolling horizon planning approach is devised. Experimental tests on real data provided by SECH terminal over a six months planning horizon highlight the effectiveness of the approach - the maximum monthly risk for workers is reduced by 33.9% compared to the current planning – and suitability to other container terminal contexts. Moreover, the model is applicable to a broad range of port situations, and robust enough to need little adaptation

Guest Editorial Special Section on Advances in Automation and Optimization for Sustainable Transportation and Energy Systems

This special section of the IEEE Transactions on Automation Science and Engineering (T-ASE) focuses on new models, methods, and technologies for energy efficiency and sustainability in transportation and energy systems. In this section, the focus is thus on articles considering sustainable transportation, such as electric vehicles (EVs), integrated with the smart grid requirements. As guest editors, we are very pleased to present the selected 12 papers, whose topics are specifically related to optimal planning of charging stations (CSs), sustainable transportation and mobility, EVs integration in smart grids, reliability, reduction of consumption, demand response and smart grid modeling, optimal scheduling, routing and charging of fleets of EVs, as well as smart parkin

Comparing Techniques for Mobile Interaction with Objects from the Real World.

Mobile interaction with objects from the real world is gaining in popularity and importance as different mobile technologies increasingly provide the basis for the extraction and usage of information from physical objects. So far, Physical Mobile Interaction is used in rather simple ways. This paper presents a comparison and evaluation of more complex and sophisticated techniques for Physical Mobile Interaction. The results indicate the importance of usability guidelines that pay attention to these new interaction techniques

Correction to: Deep reinforcement learning for multi-objective placement of virtual machines in cloud datacenters

Page 2: Column 2, lines 2-4, previously read: "Specifically, we consider a decision maker that, after a proper training, is able to select the most suitable heuristic for compute the placement for each VM requested by end users"