Abstract
Background: Internet-based cognitive-behavioral therapy (iCBT) is more effective when it is guided by human support than when it is unguided. This may be attributable to higher adherence rates that result from a positive effect of the accompanying support on motivation and on engagement with the intervention. This protocol presents the design of a pilot randomized controlled trial that aims to start bridging the gap between guided and unguided interventions. It will test an intervention that includes automated support delivered by an embodied conversational agent (ECA) in the form of a virtual coach. Methods/design: The study will employ a pilot two-armed randomized controlled trial design. The primary outcomes of the trial will be (1) the effectiveness of iCBT, as supported by a virtual coach, in terms of improved intervention adherence in comparison with unguided iCBT, and (2) the feasibility of a future, larger-scale trial in terms of recruitment, acceptability, and sample size calculation. Secondary aims will be to assess the virtual coach's effect on motivation, users' perceptions of the virtual coach, and general feasibility of the intervention as supported by a virtual coach. We will recruitN = 70 participants from the general population who wish to learn how they can improve their mood by using Moodbuster Lite, a 4-week cognitive-behavioral therapy course. Candidates with symptoms of moderate to severe depression will be excluded from study participation. Included participants will be randomized in a 1:1 ratio to either (1) Moodbuster Lite with automated support delivered by a virtual coach or (2) Moodbuster Lite without automated support. Assessments will be taken at baseline and post-study 4 weeks later. Discussion: The study will assess the preliminary effectiveness of a virtual coach in improving adherence and will determine the feasibility of a larger-scale RCT. It could represent a significant step in bridging the gap between guided and unguided iCBT interventions.

Abstract
A high production yield, Y = 1 - pfail, and thus a low failure rate, pfail, is a key requirement for successful chip design and the design of many other technical products and systems. We focus on IC design in the analog and mixedsignal domains, where Monte Carlo (MC) techniques have been a standard method for many years (see "Important Monte Carlo Rules Engineers Should Know"). Circuits have to be reliable under certain ranges of environmental parameters, such as supply voltage (V) and temperature (T). Furthermore, the set of semiconductor technology parameters (P) varies significantly, from die to die (global variations) to device to device (local variations, called mismatch). Many circuit tricks are known to minimize all of these influences (for example, using cascodes for a high power-supply rejection, differential pairs to cancel out threshold voltages, special layout techniques, and so on), but at some point problems become hard to anticipate, and further improvements are difficult to achieve. We must accept such variations and need to analyze their impact on production yield, which is a function of these parameters and the specifications (such as design topology and component sizes, among others). © 2009-2012 IEEE.

Abstract
The industrial growth of the last decades created a need for intelligent and autonomous systems that can propose solutions to scheduling problems efficiently. The job shop scheduling problem (JSSP) is the most common formulation of these real-world scheduling problems and can be found in complex fields, such as transportation or industrial assemblies, where the ability to quickly adapt to unforeseen events is critical. Using the Markov decision process mathematical framework, this paper details a formulation of the JSSP as a reinforcement learning (RL) problem. The formulation is part of a proposal of a novel environment where RL agents can interact with JSSPs that is detailed on this paper, including a comprehensive explanation of the design process, the decisions that were made and the key lessons learnt. Considering the need for better scheduling approaches on modern manufacturing environments, the limitations that current techniques have and the major breakthroughs that are being made on the field of machine learning, the environment proposed on this paper intends to be a major contribution to the JSSP landscape, enabling academics from different areas to focus on the development of new algorithms and effortlessly test them on academic and real-world benchmarks. © 2020 MIR Labs.

Abstract
Prediction of the energy consumption is a key aspect of home energy management systems, whose aim is to increase the occupant’s comfort while reducing the energy consumption. This work, employing three years measured data, uses radial basis function neural networks, designed using a multi-objective genetic algorithm (MOGA) framework, for the prediction of total electric power consumption, HVAC demand and other loads demand. The prediction horizon desired is 12 h, using 15 min step ahead model, in a multi-step ahead fashion. To reduce the uncertainty, making use of the preferred set MOGA output, a model ensemble technique is proposed which achieves excellent forecast results, comparing additionally very favorably with existing approaches. © 2020, Springer Nature Switzerland AG.

Abstract
Internet of things (IoT) is increasingly present in our lives. As a consequence of connecting devices, IoT can make people's lives more convenient and comfortable. However, despite unquestionable benefits offered by IoT, there is still a great deal of concern from users and companies about the security and privacy of their data. In this sense, this study conducts a qualitative study based on three case studies of companies in the IoT field, which aims to characterize how these IoT companies look at the security and privacy challenges posed by IoT. The findings allowed the authors to identify the main challenges faced by IoT companies during the past years, the main privacy risks exposed by IoT devices, and the countermeasures that companies and users can adopt to increase the security of IoT.

Abstract
Ambient radioactivity and atmospheric electricity are inextricably linked phenomena. In order to assess the role of ambient radioactivity in the local variability of the atmospheric electric field at an urban site, simultaneous measurements of radon concentration, gamma radiation, and atmospheric electric field are carried out in the city of Porto, Portugal. Both radon and gamma radiation display an average daily cycle peaking before sunrise, but with considerable variability from day to day, particularly in amplitude. The atmospheric electric field displays a daily cycle with a minimum at dawn and maximum in the early afternoon, as well as a secondary peak in the early morning. The temporal variation of the daily patterns is analysed by means of an empirical orthogonal function analysis, and related to local meteorological parameters. The variability of the local atmospheric electric field is mainly determined by aerosol transport and accumulation close to the surface associated with local meteorological conditions and atmospheric stability rather than by conductivity variations associated with ambient radioactivity.