Abstract
In the upcoming years, European countries have to make a strong bet on solar energy. Small photovoltaic systems are able to provide energy for several applications like housing, traffic and street lighting, among others. This field is expected to have a big growth, thus taking advantage of the largest renewable energy source existing on the planet, the sun. This paper proposes a computational model able to simulate the behavior of a stand-alone photovoltaic system. The developed model allows to predict PV systems behavior, constituted by the panels, storage system, charge controller and inverter, having as input data the solar radiation and the temperature of the installation site. Several tests are presented that validates the reliability of the developed model.

Abstract
Purpose The purpose of this paper is to present a programming by demonstration (PbD) system based on 3D stereoscopic vision and inertial sensing that provides a cost-effective pose tracking system, even during error-prone situations, such as camera occlusions. Design/methodology/approach The proposed PbD system is based on the 6D Mimic innovative solution, whose six degrees of freedom marker hardware had to be revised and restructured to accommodate an IMU sensor. Additionally, a new software pipeline was designed to include this new sensing device, seeking the improvement of the overall system's robustness in stereoscopic vision occlusion situations. Findings The IMU component and the new software pipeline allow the 6D Mimic system to successfully maintain the pose tracking when the main tracking tool, i.e. the stereoscopic vision, fails. Therefore, the system improves in terms of reliability, robustness, and accuracy which were verified by real experiments. Practical implications Based on this proposal, the 6D Mimic system reaches a reliable and low-cost PbD methodology. Therefore, the robot can accurately replicate, on an industrial scale, the artisan level performance of highly skilled shop-floor operators. Originality/value To the best of the authors' knowledge, the sensor fusion between stereoscopic images and IMU applied to robot PbD is a novel approach. The system is entirely designed aiming to reduce costs and taking advantage of an offline processing step for data analysis, filtering and fusion, enhancing the reliability of the PbD system.

Abstract
The seashore is the front door to the oceans and the sustain of many societies. However, humans still seem to be unable to unlock new paradigms to project sustainable growth of marine and coastal ecosystems. One of the reasons for this is the lack of knowledge about the natural processes that systematically change their balance. Thus, a new generation of tools is needed to gather data to validate and predict geostatistical models and protect this important resource. This manuscript reports the design and validation of a multi-parameter marine station installed in the estuary of Cavado - Portugal. For the last two years, the station has hosted several own-developed sensors to monitor water parameters, and it was designed to send the monitoring data, in real-time, to a public website so the information can be shared with the communities. So far, the monitoring station has been able to produce data about hydraulic and environmental dynamics, such as water column height or sediment displacement, as well as seasonal events and other extreme phenomena occurrences such as floods. The proposed monitoring system, built in a low-power and low-cost philosophy, aims to allow massive replication all over the coastal areas and to deliver qualitative and quantitative data for better management and planning of the littoral.

Abstract
Wide availability of edge computing platforms, as expected in emerging 5G networks, enables a computing continuum between centralized cloud services and the edge of the network, close to end-user devices. This is particularly appealing for online analytics as data collected by devices is made available for decisionmaking. However, cloud-based parallel-distributed data processing platforms are not able to directly access data on the edge. This can be circumvented, at the expense of freshness, with data synchronization that periodically uploads data to the cloud for processing. In this work, we propose an adaptive database synchronization system that makes distributed data in edge nodes available dynamically to the cloud by balancing between reducing the amount of data that needs to be transmitted and the computational effort needed to do so at the edge. This adapts to the availability of CPU and network resources as well as to the application workload.

Abstract

Abstract
In the digital world, the demand for better interactions between subscribers and companies is growing, creating the need for personalized and individualized experiences. With the exponential growth of email usage over the years, broad flows of campaigns are sent and received by subscribers, which reveals itself to be a problem for both companies and subscribers. In this work, subscribers are segmented by their behaviors and profiles, such as (i) open rates, (ii) click-through rates, (iii) frequency, and (iv) period of interactions with the companies. Different regressions are used: (i) Random Forest Regressor, (ii) Multiple Linear Regression, (iii) K-Neighbors Regressor, and (iv) Support Vector Regressor. All these regressions’ results were aggregated into a final prediction achieved by an ensemble approach, which uses averaging and stacking methods. The use of Long Short-Term Memory is also considered in the presented case. The stacking model obtained the best performance, with an R (Formula presented.) score of 0.91 and a Mean Absolute Error of 0.204. This allows us to estimate the week’s days with a half-day error difference. This work presents promising results for subscriber segmentation based on profile information for predicting the best period for email marketing. In the future, subscribers can be segmented using the Recency, Frequency and Monetary value, the Lifetime Value, or Stream Clustering approaches that allow more personalized and tailored experiences for subscribers. The latter tracks segments over time without costly recalculations and handles continuous streams of new observations without the necessity to recompile the entire model. © 2022 by the authors.