Abstract
Automatic Root Cause Analysis solutions aid analysts in finding problems' root causes by using automatic data analysis. When trying to locate the root cause of a problem in a manufacturing process, an issue-denominated overlap can occur. Overlap can impede automated diagnosis using algorithms, as the data make it impossible to discern the influence of each machine on the quality of products. This paper proposes a new measure of overlap based on an information theory concept called Positive Mutual Information. This new measure allows for a more detailed analysis. A new approach is developed for automatically finding the root causes of problems when overlap occurs. A visualization that depicts overlapped locations is also proposed to ease practitioners' analysis. The proposed solution is validated in simulated and real case-study data. Compared to previous solutions, the proposed approach improves the capacity to pinpoint a problem's root causes.

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Abstract
Systems for wireless energy transmission (WET) are gaining prominence nowadays. This work presents a WET system based on the photo-thermoelectric effect. With an incident laser beam at lambda = 1450 nm, a temperature gradient is generated in the radial flexible thermoelectric (TE) device, with a carbon-based light collector in its center to enhance the photoheating. The three-part prototype presents a unique approach by using a radial TE device with one simple manufacturing process - screen-printing. A TE ink with a polymeric matrix of poly(3,4-ethylenedioxythiophene) polystyrene sulfonate and doped-Poly(vinyl alcohol) with Sb-Bi-Te microparticles is developed (S similar to 33 mu VK-1 and s similar to 10.31 Sm-1), presenting mechanical and electrical stability. Regarding the device, a full electrical analysis is performed, and the influence of the light collector is investigated using thermal tests, spectrophotometry, and numerical simulations. A maximum output voltage (Vout) of similar to 16 mV and maximum power density of similar to 25 mu Wm(-2) are achieved with Plaser = 2 W. Moreover, the device's viability under extreme conditions is explored. At T similar to 180 K, a 25% increase in Vout compared to room-temperature conditions is achieved, and at low pressures (similar to 10(-6) Torr), an increase of 230% is obtained. Overall, this prototype allows the supply of energy at long distances and remote places, especially for space exploration.

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The article focuses on the consumption of goods used by consumers of different generations from 3 different countries: Albania, Polish and Portugal. The aim of the analysis was to identify respondents"indications concerning: (1) knowledge of the definition of the circular economy, (2) declared by respondents places of purchase of used products and (3) type of purchased products used by respondents. The analysis was conducted among 495 respondents from Albania, Polish and Portugal representing three generations (X, Y, Z). Correspondence analysis was used for statistical data analysis. Statistically significant differences in knowledge of the definition of the circular economy were shown between respondents from Albania, Polish and Portugal. It was also found that respondents"preferences regarding the place of purchase of second-hand goods are differentiated (at a statistically significant level) by nationality and year of birth (generation). The obtained results open the possibility of further research aimed at identifying different behaviors among these groups of consumers. The presented work, both in the cognitive and application part, can be a source of knowledge and popularization of research, as well as a source of inspiration for in-depth reflection and scientific discussion. The analyses presented in the publication may complement the existing research in the field of circular economy. Extending the survey to other EU countries can help define a strategy for policymakers, manufacturers and retailers to make greater use of circular economy solutions, while maintaining the viability of their operations. © 2023 Altin Kulli et al., published by Sciendo.

Abstract
In the last years, the number of machine learning algorithms and their parameters has increased significantly. On the one hand, this increases the chances of finding better models. On the other hand, it increases the complexity of the task of training a model, as the search space expands significantly. As the size of datasets also grows, traditional approaches based on extensive search start to become prohibitively expensive in terms of computational resources and time, especially in data streaming scenarios. This paper describes an approach based on meta-learning that tackles two main challenges. The first is to predict key performance indicators of machine learning models. The second is to recommend the best algorithm/configuration for training a model for a given machine learning problem. When compared to a state-of-the-art method (AutoML), the proposed approach is up to 130x faster and only 4% worse in terms of average model quality. Hence, it is especially suited for scenarios in which models need to be updated regularly, such as in streaming scenarios with big data, in which some accuracy can be traded for a much shorter model training time.

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