Abstract
While in the past the primary goal to optimize software was the run time optimization, nowadays there is a growing awareness of the need to reduce energy consumption. Additionally, a growing number of developers wish to become more energy-aware when programming and feel a lack of tools and the knowledge to do so. In this paper we define a ranking of energy efficiency in programming languages. We consider a set of computing problems implemented in ten well-known programming languages, and monitored the energy consumed when executing each language. Our preliminary results show that although the fastest languages tend to be the lowest consuming ones, there are other interesting cases where slower languages are more energy efficient than faster ones.

Abstract
Entry-only Automatic Fare Collection (AFC) systems are widely used in urban transports. Its main advantages include easy usability by passengers, improvement of the efficiency of revenue management, adequacy to integrate inter-modality approaches, easy cooperation between operators, systematic data collection and gathering tools, contributing to improve the planning process. This work starts with the literature review on applications of the Trip-Chaining Method (TCM) to the estimation of Origin-Destination (OD) matrices using entry-only AFC data. The main contribution of this study is to provide an OD matrix for the city of Porto, allowing to improve the quality of its public transport system. The paper reports the implementation of the TCM to estimate the alighting locations at the disaggregated level in the case study of Porto. The main assumptions adopted are: passengers start the next journey stage at or near the alighting location of their previous trip, passengers end the last trip of the day at the boarding location of the first trip of the day, passengers can only alight in the sequence of stops not yet traveled by the route / direction they boarded, passengers have a maximum interchange distance, above which the destination of that journey stage is not inferred. © 2017 The Authors. Published by Elsevier B.V.

Abstract
Sophistication and logical depth are two measures that express how complicated the structure in a string is. Sophistication is defined as the minimal complexity of a computable function that defines a two-part description for the string that is shortest within some precision; the second can be defined as the minimal computation time of a program that is shortest within some precision. We show that the Busy Beaver function of the sophistication of a string exceeds its logical depth with logarithmically bigger precision, and that logical depth exceeds the Busy Beaver function of sophistication with logarithmically bigger precision. We also show that sophistication is unstable in its precision: constant variations can change its value by a linear term in the length of the string.

Abstract
Ethionamide (ETH) is an important second-line antituberculosis drug used for the treatment of patients infected with multidrug-resistant Mycobacterium tuberculosis. Recently, we reported that the loading of ETH into thermally carbonized-porous silicon (TCPSi) nanoparticles enhanced the solubility and permeability of ETH at different pH-values and also increased its metabolization process. Based on these results, we synthesized carboxylic acid functionalized thermally hydrocarbonized porous silicon nanoparticles (UnTHCPSi NPs) conjugated with ETH and its antimicrobial effect was evaluated against Mycobacterium tuberculosis strain H37Rv. The activity of the conjugate was increased when compared to free-ETH, which suggests that the nature of the synergy between the NPs and ETH is likely due to the weakening of the bacterial cell wall that improves conjugate-penetration. These ETH-conjugated NPs have great potential in reducing dosing frequency of ETH in the treatment of multidrug-resistant tuberculosis (MDR-TB).

Abstract
This paper proposes a case-based reasoning methodology to automatically choose the most appropriate optimization algorithms and respective parameterizations to solve the problem of optimal resource scheduling in smart energy grids. The optimal resource scheduling is, however, a heavy computation problem, which deals with a large number of variables. Moreover, depending on the time horizon of this optimization, fast response times are usually required, which makes it impossible to apply traditional exact optimization methods. For this reason, the application of metaheuristic methods is the natural solution, providing near-optimal solutions in a much faster execution time. Choosing which optimization approaches to apply in each time is the focus of this work, considering the requirements for each problem and the information of previous executions. A case-based reasoning methodology is proposed, considering previous cases of execution of different optimization approaches for different problems. A fuzzy logic approach is used to adapt the solutions considering the balance between execution time and quality of results Copyright © 2017 for this paper by its authors.

Abstract