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Robert Dochow mathematically derives a simplified classification structure of selected types of the portfolio selection problem. He proposes two new competitive online algorithms with risk management, which he evaluates analytically. The author empirically evaluates online algorithms by a comprehensive statistical analysis. Concrete results are that follow-the-loser algorithms show the most promising performance when the objective is the maximization of return on investment and risk-adjusted performance. In addition, when the objective is the minimization of risk, the two new algorithms with risk management show excellent performance. A prototype of a software tool for automated evaluation of algorithms for portfolio selection is given. Contents • Performance Evaluation • Selected Algorithms from the Literature • Proposed Algorithms with Risk Management • Empirical Testing of Algorithms• A Software Tool for Testing Target Groups • Scientists and students from the fields of finance, operations research, and machine learning • Practitioners in banks and insurance companies, traders and brokers The Author Dr. Robert Dochow completed his dissertation under the supervision of Prof. Dr. Günter Schmidt at the Chair of Operations Research and Business Informatics of Saarland University, Saarbrücken, Germany

abstract: On October 19th, 2015, Canadian citizens will head to the polls in their country's 42nd general election. The vote offers the choice between the existing right wing government (Stephen Harper's Conservatives), three largely left-of-center alternatives (the New Democrats, Liberals, and Greens), a regional party (the Bloc Québécois), and several independents and minor parties. At the same time, like in many other countries, Canada is plagued by concerns about voter apathy and lack of participation, especially among younger demographics. ; The final version of this article, as published in Engaging Science, Technology, and Society, can be viewed online at: http://estsjournal.org/article/view/27

The navigation and localization of autonomous underwater vehicles (AUVs) in seawater are of the utmost importance for scientific research, petroleum engineering, search and rescue, and military missions concerning the special environment of seawater. However, there is still no general method for AUVs navigation and localization, especially in the featureless seabed. The reported approaches to solving AUVs navigation and localization problems employ an expensive inertial navigation system (INS), with cumulative errors and dead reckoning, and a high-cost long baseline (LBL) in a featureless subsea. In this study, a simultaneous localization and mapping (AMB-SLAM) online algorithm, based on acoustic and magnetic beacons, was proposed. The AMB-SLAM online algorithm is based on multiple randomly distributed beacons of low-frequency magnetic fields and a single fixed acoustic beacon for location and mapping. The experimental results show that the performance of the AMB-SLAM online algorithm has a high robustness. The proposed approach (the AMB-SLAM online algorithm) provides a low-complexity, low-cost, and high-precision online solution to the AUVs navigation and localization problem in featureless seawater environments. The AMB-SLAM online solution could enable AUVs to autonomously explore or autonomously intervene in featureless seawater environments, which would enable AUVs to accomplish fully autonomous survey missions.

Energy harvesting has emerged as an appealing technology to recharge battery powered devices. Recently, an extensive research has been conducted on the design of power allocation policies for energy harvesting devices. Most works have focused on offline policies that assume non-causal knowledge of the energy harvesting process. Only a few works have considered online policies with the more realistic assumption of only having past knowledge of the energy harvesting process; however, these works generally incur an additional assumption on the knowledge of the probability distribution of the harvested energy (e.g. Poisson distribution) leading to online algorithms that are rarely applicable with available energy harvesting technologies. This paper proposes three online power allocation algorithms capable of learning from the harvested energy in previous days and that perform, in average, as well as the best fixed offline strategy. The numerical results validate the performance of the proposed algorithms when energy is harvested through solar panels. ; Grant numbers : This work is partially supported by the Spanish Government through the projects INTENSYV (TEC2013-44591-P) and E-CROPs (PCIN-2013-027) and by the Catalan Government (2014 SGR 1567).© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

I. Introduction -- II. Principles -- III. Algorithms -- IV. Empirical studies -- V. Conclusion