Abstract
Modern containerized distributed systems, such as big data storage and processing stacks or micro-service based applications, are inherently hard to monitor and optimize, as resource usage does not directly match hardware resources due to multiple virtualization layers. For instance, inter-application traffic is an important factor in as it directly indicates how components interact, it has not been possible to accurately monitor it in an application independent way and without severe overhead, thus putting it out of reach of cloud platforms. In this paper we present an efficient black-box monitoring approach for gathering detailed structural information of collaborating processes in a distributed system that can be queried for various purposes, as it includes both information about processes, containers, and hosts, as well as resource usage and amount of data exchanged. The key to achieving high detail and low overhead without custom application instrumentation is to use a kernel-aided event driven strategy. We validate a prototype implementation by applying it to multi-platform microservice deployments, evaluate its performance with micro-benchmarks, and demonstrate its usefulness for container placement in a distributed data storage and processing stack (i.e., Cassandra and Spark).

Abstract
This paper analyzes the activation of the manual balancing reserve of the Portuguese system and its prices for the period 2015-2017. Standard, logistic and LASSO regression models, causal analysis based on Bayesian networks and random forests are applied. Results show that the variables that better explain the activation of the manual reserve are the imbalances of both renewable generation and demand, but surprisingly forecasted with persistence models based on the last verified measurements (available 15 minutes before the reserve activation), instead of using more elaborated models based on production forecasts. Prices, however, are harder to explain suggesting the need for additional information, such as bidding prices not used in this study.

Abstract
Medical image quality assessment plays an important role not only in the design and manufacturing processes of image acquisition but also in the optimization of decision support systems. This work introduces a new deep ordinal learning approach for focus assessment in whole slide images. From the blurred image to the focused image there is an ordinal progression that contains relevant knowledge for more robust learning of the models. With this new method, it is possible to infer quality without losing ordinal information about focus since instead of using the nominal cross-entropy loss for training, ordinal losses were used. Our proposed model is contrasted against other state-of-the-art methods present in the literature. A first conclusion is a benefit of using data-driven methods instead of knowledge-based methods. Additionally, the proposed model is found to be the top-performer in several metrics. The best performing model scores an accuracy of 94.4% for a 12 classes classification problem in the FocusPath database.

Abstract
Digital exposure to the Internet among the younger generations, notwithstanding their digital abilities, has increased and raised the alarm regarding the need to intensify the education on cybersecurity in schools. Understanding of the human factor and its influence on children, namely their attitudes and behaviors online, is pivotal to reinforce their awareness towards cyberattacks, and to promote their digital citizenship. This paper aims to present an integrated cybersecurity and cyberawareness strategy composed of three major steps: (1) Cybersecurity attitude and behavior assessment, (2) self-diagnosis, and (3) teaching/learning activities. The following contributions are made: Two questionnaires to assess risky attitudes and behaviors regarding cybersecurity; a self-diagnosis to measure students' skills on cybersecurity; a lesson plan addressing cyberawareness to be applied on Information and Communications Technology (ICT) and citizenship education curricular units. Cybersecurity risky attitudes and behaviors were evaluated in a junior high school population of 164 students attending the sixth and ninth grades. The assessment focused on two main subjects: To identify the attitudes and behaviors that raise the risk on cybersecurity among the participating students; to characterize the acquired students' cybersecurity and cyberawareness skills. Global and individual scores and the histograms for attitudes and behaviors are presented. The items in which we have observed significant differences between sixth and ninth grades are depicted and quantified by their corresponding p-values obtained through the Mann-Whitney non-parametric test. Regarding the results obtained on the assessment of attitudes and behaviors, although positive, we observed that the attitudes and behaviors in ninth grade students are globally inferior compared to those attained by sixth grade students. The deployed strategy for cyberawareness was applied in a school context; however, the same approach is suitable to be applied in other types of organizations, namely enterprises, healthcare institutions and public sector.

Abstract
—The main objective of this work is to summarize the most commonly used dielectric fluids in the power distribution transformers, as well as to discuss what are the latest and the rationale behind those trends. The favorable and unfavorable reasons for any choice behind each of those dielectric fluids will be discussed. Additionally, this work also advances the power distribution transformers health index most commonly used to assess the condition of the transformer.

Abstract
In this paper, the relevance of integrated planning concerning decisions of production and blending in a spinning industry is studied. The scenario regards a plant that produces several yarn packages over a planning horizon. Each yarn type is produced using a blend of several cotton bales that must contain attributes to ensure the quality of the produced yarns. Three approaches to managing production and blending are compared; the first deals with the solution to the production scheduling and blending problems in a single integrated model. The second approach hierarchically addresses these problems. The third procedure combines features from the integrated and hierarchical approaches. These approaches are applied to a real-world problem, and their respective performances are analyzed. The third approach proved to deal with lot sizing, scheduling and blending in the spinning industry more efficiently. Moreover, the results indicate the importance of coordinating production and blending decisions. © 2021 Brazilian Operations Research Society.