Abstract
The aim of this study was to test the effectiveness of invitation to cervical cancer screening through a very low-cost strategy based on automated and customized text messages, phone calls and reminders. A randomized (1:1) controlled trial was conducted among 13 Portuguese primary care units, recruiting women aged 25 to 49 years, eligible for cervical cancer screening, with an available mobile phone number. In the intervention group, participants were invited for cervical cancer screening through automated/customized text messages and phone calls, followed by text message reminders. Participants in the control group were invited through a written letter (standard of care). The primary outcome was the proportion of women adherent to screening up to 45 days after invitation and the secondary outcome was defined as the adherence proportion after invitation based only on text messages and reminders. A total of 1220 women were randomized, 605 to intervention and 615 to control group. The adherence to cervical cancer was significantly higher among women assigned to intervention (39.0% vs. 25.7%, p < 0.001); this corresponds to a difference of 13.3% (95% CI 8.1 to 18.5). The difference in adherence between an invitation strategy based only on text messages and reminders and the standard of care was - 0.4%, 95% CI -5.3 to 4.5. In conclusion, an invitation to cervical cancer screening using automated text messages/phone calls and reminders increases the adherence to cervical cancer screening. Such a low-cost and operator-independent strategy of invitation may contribute to the sustainability of organized screening programs. Trial registration number:NCT03122275.

Abstract
Dizajn distributivne mreže predstavlja jednu od strateških odluka u konkurenciji za kompanije visokog uticaja. Optimalna lokacija objekata u odnosu na kapacitet ponude i potražnje omogucuje visokom nivou usluga prisustvo na tržištu. U ovom radu predstavljen je metodološki okvir za projektovanje distributivnih mreža kombinovanjem primene tehnika klastera i matematickog programiranja. Predložena metodologija je testirana sa realnim podacima dobijenim od kompanije bezalkoholnih pica u Kolumbiji. Pristup razmatra tri glavne faze. U prvoj fazi, proces klasteriranja kupaca vrši se pomocu K-sredstava kako bi se dobila lokacija za potencijalne distributivne centre (DC). U drugoj fazi, model za dizajn distributivne mreže se vrši pomocu mešovitog programiranja celih brojeva (MPCB) razmatrajuci razlicite opcije za dodeljivanje DC-ma. U finalnoj fazi vrši se procena predložene metodologije u realnom slucaju. Kao rezultat, definisana je distributivna šema koja omogucava ulaz u nova tržišna podrucja sa efikasnom strategijom za prodiranje proizvoda u velike gradove kao što je Bogota u Kolumbiji.

Abstract
This paper introduces the TSL language (short name for “Test Specification Language”) that intends to improve the test specification of information systems in a systematic, rigorous and consistent way. TSL specifications are produced from close requirement specifications expressed in the RSL language (Requirements Specification Language). Both RSL and TSL support human-readable executable specifications closer to natural language than models usually used in model-based testing approaches. TSL includes several constructs logically arranged into views according to multiple testing engineering strategies, commonly found in the information systems domain, such as: data entity tests and state machine tests, all of them produced from equivalent requirement specification in RSL. A case study is also presented to illustrate the proposed approach.

Abstract
This study presents a technique that uses an interactive virtual environment for the rehabilitation treatment of patients with mechanical strabismus and/or amblyopia who have lost eye movement. The relevant part of this treatment is the act of forcing the two eyes to cooperate with each other by increasing the level of adaptation of the brain and allowing the weak eye to see again. Accordingly, the game enables both eyes to work together, providing the patient with better visual comfort and life quality. In addition, the virtual environment is attractive and has the ability to overcome specific challenges with real-time feedback, coinciding with ideal approaches for use in ocular rehabilitation. The entire game was developed with free software and the 3D environment, which is made from low-cost virtual reality glasses, as well as Google Cardboard which uses a smartphone for the display of the game. The method presented was tested in 41 male and female patients, aged 8 to 39 years, and resulted in the success of 40 patients. The method proved to be feasible and accessible as a tool for the treatment of amblyopia and strabismus. The project was registered in the Brazil platform and approved by the ethics committee of the State University of Piaui-UESPI, with the CAAE identification code: 37802114.8.0000.5209.

Abstract
The algorithm selection problem refers to the ability to predict the best algorithms for a new problem. This task has been often addressed by Metalearning, which looks for a function able to map problem characteristics to the performance of a set of algorithms. In the context of Collaborative Filtering, a few studies have proposed and validated the merits of different types of problem characteristics for this problem (i.e. dataset-based approach): using systematic metafeatures and performance estimations obtained by subsampling landmarkers. More recently, the problem was tackled using Collaborative Filtering models in a novel framework named CF4CF. This framework leverages the performance estimations as ratings in order to select the best algorithms without using any data characteristics (i.e algorithm-based approach). Given the good results obtained independently using each approach, this paper starts with the hypothesis that the integration of both approaches in a unified algorithm selection framework can improve the predictive performance. Hence, this work introduces CF4CF-META, an hybrid framework which leverages both data and algorithm ratings within a modified Label Ranking model. Furthermore, it takes advantage of CF4CF’s internal mechanism to use samples of data at prediction time, which has proven to be effective. This work starts by explaining and formalizing state of the art Collaborative Filtering algorithm selection frameworks (Metalearning, CF4CF and CF4CF-META) and assess their performance via an empirical study. The results show CF4CF-META is able to consistently outperform all other frameworks with statistically significant differences in terms of meta-accuracy and requires fewer landmarkers to do so. © 2018, Springer Nature Switzerland AG.

Abstract
LED-based technology is transforming public lighting networks, favouring smart city innovations. Beyond energy efficiency benefits, LED-based luminaries provide real time stateful data. However, most of the municipalities manage all their luminaries equally, independently of its state or the environmental conditions. Some existing approaches to street lighting management are already considering elementary features such as on-off control and individual dimming based on movement or ambient light. Nevertheless, our vision on public (street) lighting management, goes beyond basic consumption monitoring and dimming control, encompassing: a) adaptive lighting, by considering other potential influence factors such as work temperature of the luminaries or the arrangement of the luminaries on the street; b) Colour tuning, for public safety purposes and; c) emergency behaviour control. This paper addresses the first component (adaptive lighting) influence factors, in the scope of a real scenario in a Portuguese municipality.