Abstract
Objective: A systematic literature review was conducted to identify relevant ergonomic and safety factors for designing collaborative workspaces in industrial settings. Background: The growing use of smart and collaborative robots in manufacturing brings some challenges for the human-robot interaction design. Human-centered manufacturing solutions will improve physical and mental well-being, performance, productivity and sustainability. Method: A systematic review of the literature was performed based on the protocol of Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Results: After a search in the databases Scopus and Web of Science, applying inclusion and exclusion criteria, 33 publications in the English language, published between the years 2010 and 2020, remained in the final analysis. Publications were categorized in cognitive ergonomic factors (13), safety factors (10), physical ergonomic factors (6) and organizational ergonomic factors (4). The analysis of results reinforced that to optimize the design of collaborative workstations it is imperative to have a holistic perspective of collaboration, integrating multiple key factors from areas such as engineering, ergonomics, safety, sociology and psychological as well as manufacturing efficiency and productivity. Application: Considering the advantages of the use of cobots in manufacturing, the results of this review will be useful to support companies in implementing human-robot collaboration. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
This paper describes a system designed to collect water samples, from the surface down to a configurable depth, and with configurable profiles of vertical velocity. The design was intended for the analysis of suspended sediments, therefore the sampling can integrate water flow for a given depth profile, or at a specific depth. The system is based on a catamaran-shaped platform, from which a towfish is lowered to collect the water samples. The use of a surface vehicle ensures a permanent link between the operator and the full system, allowing for a proper mission supervision. All components can be remotely controlled from the control station, or programmed for fully autonomous operation. Although the main intended use is for the analysis of suspended sediments in rivers, it can easily be extended to collect water samples in other water bodies.

Abstract
Pseudomonas syringae pv. actinidiae (Psa) has been responsible for numerous epidemics of bacterial canker of kiwi (BCK), resulting in high losses in kiwi production worldwide. Current diagnostic approaches for this disease usually depend on visible signs of the infection (disease symptoms) to be present. Since these symptoms frequently manifest themselves in the middle to late stages of the infection process, the effectiveness of phytosanitary measures can be compromised. Hyperspectral spectroscopy has the potential to be an effective, non-invasive, rapid, cost-effective, high-throughput approach for improving BCK diagnostics. This study aimed to investigate the potential of hyperspectral UV-VIS reflectance for in-situ, non-destructive discrimination of bacterial canker on kiwi leaves. Spectral reflectance (325-1075 nm) of twenty plants were obtained with a handheld spectroradiometer in two commercial kiwi orchards located in Portugal, for 15 weeks, totaling 504 spectral measurements. Several modeling approaches based on continuous hyperspectral data or specific wavelengths, chosen by different feature selection algorithms, were tested to discriminate BCK on leaves. Spectral separability of asymptomatic and symptomatic leaves was observed in all multi-variate and machine learning models, including the FDA, GLM, PLS, and SVM methods. The combination of a stepwise forward variable selection approach using a support vector machine algorithm with a radial kernel and class weights was selected as the final model. Its overall accuracy was 85%, with a 0.70 kappa score and 0.84 F-measure. These results were coherent with leaves classified as asymptomatic or symptomatic by visual inspection. Overall, the findings herein reported support the implementation of spectral point measurements acquired in situ for crop disease diagnosis.

Abstract
A smart grid is the future vision of power systems that will be enabled by artificial intelligence (AI), big data, and the Internet of things (IoT), where digitalization is at the core of the energy sector transformation. However, smart grids require that energy managers become more concerned about the reliability and security of power systems. Therefore, energy planners use various methods and technologies to support the sustainable expansion of power systems, such as electricity demand forecasting models, stochastic optimization, robust optimization, and simulation. Electricity forecasting plays a vital role in supporting the reliable transitioning of power systems. This paper deals with short-term load forecasting (STLF), which has become an active area of research over the last few years, with a handful of studies. STLF deals with predicting demand one hour to 24 h in advance. We extensively experimented with several methodologies from machine learning and a complex case study in Panama. Deep learning is a more advanced learning paradigm in the machine learning field that continues to have significant breakthroughs in domain areas such as electricity forecasting, object detection, speech recognition, etc. We identified that the main predictors of electricity demand in the short term: the previous week's load, the previous day's load, and temperature. We found that the deep learning regression model achieved the best performance, which yielded an R squared (R-2) of 0.93 and a mean absolute percentage error (MAPE) of 2.9%, while the AdaBoost model obtained the worst performance with an R-2 of 0.75 and MAPE of 5.70%.

Abstract
The entrepreneurs responsible for establishing university spinoffs are incessantly looking for new ways to leverage existing technology or create an entirely new product or service market. The creation of disruptive innovative solutions has assumed a key role in enhancing the role of university spinoffs in the global marketplace. This study aims to characterize and explore the phenomenon of disruptive innovation in university spinoffs and identify how university spinoffs recognize and evaluate low-end and new-market disruptive technologies. The findings reveal that university spin-offs have essentially privileged new-market disruptive technologies. Participation in trade fairs, conferences and journals have been the main methods adopted by these organizations to identify disruptive technologies, while the evaluation of the potential of these disruptive technologies is essentially based on the experience and scientific knowledge of the founders of the university spin-offs. Furthermore, the size and number of years of activity of university spinoffs are two factors that allow us to understand the greater proximity of the younger and smaller spinoffs with universities and research centers, whereas the larger organizations prioritize mainly market analysis and product research techniques.

Abstract
This paper proposes an energy community management system for local energy sharing with grid flexibility services to solve the potential grid constraints of the local distribution network. A three-stage model is proposed. Stage 1 is the individual minimization of the energy bill of each prosumer by optimizing the schedules of its battery. The second stage optimizes the energy bill of the energy community by sharing internally the prosumers energy surplus and re-dispatching their batteries, while guaranteeing that each new individual prosumer energy bill is always equal or less than its stage 1 bill. The third stage is performed by the DSO to solve the grid constraints by re-dispatching the batteries, curtailing local generation or reducing consumption. Stage 3 minimizes the impact on stage 2 by minimizing the loss of profit or utility of every prosumer which is compensated accordingly.