Abstract
Higher Education Institutions use social media as a marketing channel to attract and engage users so that the institution is promoted and thus a wide range of benefits can be achieved. These institutions are evaluated globally on various success parameters, being published in rankings. In this paper, we analyze the publishing strategies and compare the results with their overall ranking positions. The results show that there is a tendency to find a particular strategy in the top ranked universities. We also found cases where the strategies are less prominent and do not match the ranking positions.

Abstract
Underwater autonomous manipulation is the capability of a mobile robot to perform intervention tasks that require physical contact with unstructured environments without continuous human supervision. Being difficult to assess the behaviour of existing motion planner algorithms, this research proposes a new planner evaluation metric to identify well-behaved planners for specialized tasks of inspection and monitoring of man-made underwater structures. This metric is named NEMU and combines three different performance indicators: effectiveness, safety and adaptability. NEMU deals with the randomization of sampling-based motion planners. Moreover, this article presents a benchmark of multiple planners applied to a 6 DoF manipulator operating underwater. Results conducted in real scenarios show that different planners are better suited for different tasks. Experiments demonstrate that the NEMU metric can be used to distinguish the performance of planners for particular movement conditions. Moreover, it identifies the most promising planner for collision-free motion planning, being a valuable contribution for the inspection of maritime structures, as well as for the manipulation procedures of autonomous underwater vehicles during close range operations.

Abstract

Abstract
<p>Volcanic gases and particulate matter (PM) can be hazardous for population not only during an eruptive event, but also during the post-eruption phase, even at significant distances from the volcanic edifice. Volcanic plume dispersion can be affected by diverse factors, such as the weather conditions (<em>e.g</em>., wind speed and direction, rainfall) and/or the topography. Several studies have showed that gas concentrations and PM impacts on the quotidian life during a volcanic crisis can be significant, highlighting the importance of setting up permanent monitoring systems.</p><p>Instruments with carbon dioxide (CO<sub>2</sub>), sulphur dioxide (SO<sub>2</sub>)<sub></sub>and particulate matter (PM<sub>2.5</sub> and PM<sub>10</sub>) low-cost sensors were developed in order to easily and continuously monitor any volcanic area, and the 2021 Cumbre Vieja eruption was chosen as test site to deploy and validate the instrumentation. A network of nine instruments was set up around the volcanic eruption site, covering both the north and south areas of the lava flows, at distances varying between 1.6 and 7 km from the volcano craters. Five instruments were designed to work autonomously in the field, powered by batteries, and the electrical network powered the other four sensors. All nine instruments broadcasted the recorded data via LoRa communication.</p><p>The network settled after the 9<sup>th</sup> December 2021, closer to the ending of the eruptive period, recorded maximum CO<sub>2</sub> concentrations of 1585 ppm at station named “Perm-2”, located at about 4.8 km distance from the volcanic craters, on the 21<sup>st</sup> December 2021. Regarding particulate matter, even if the 24 hour-mean standards set by the World Health Organization (WHO) for the PM<sub>2.5</sub> and PM<sub>10</sub> (25 mg/m<sup>3</sup> and 50 mg/m<sup>3</sup>, respectively) were not exceeded during the monitored period, maximum concentrations were also recorded for these two parameters (470 and 874 mg/m<sup>3</sup>) at “Perm-2” in the 21<sup>st</sup> December. For the same period, the station located closer to the volcano craters measured maximum SO<sub>2</sub> concentrations of 1.11 ppm. Maximum PM values were recorded also at other two monitoring sites in the same day, suggesting spatial and temporal correlation between the different parameters. In this particular case, and considering that maximum concentrations were registered during the night in the exclusion zone, one can reject the potential association of the measured values with suspended ashes resulting from sweeping and cleaning activities. For other periods, particularly after the ending of the eruption, this association must be considered. The highest concentrations of particles post-eruption were measured in the 31<sup>st</sup> December 2021 and 3<sup>rd</sup> January 2022.</p><p>The installed instruments seem to be adequate for an easier and faster deploy during a volcanic crises, allowing recognizing the presence of hazardous gas and particulate matter concentrations, crucial to reduce potential health effects on the population, even after the end of the eruptive phase.</p>

Abstract
Marine origin polymers represent a sustainable and natural alternative to mammal counterparts regarding the biomedical application due to their similarities with proteins and polysaccharides present in extracellular matrix (ECM) in humans and can reduce the risks associated with zoonosis and overcoming social- and religious-related constraints. In particular, collagen-based biomaterials have been widely explored in tissue engineering scaffolding applications, where cryogels are of particular interest as low temperature avoids protein denaturation. However, little is known about the influence of the parameters regarding their behavior, i.e., how they can influence each other toward improving their physical and chemical properties. Factorial design of experiments (DoE) and response surface methodology (RSM) emerge as tools to overcome these difficulties, which are statistical tools to find the most influential parameter and optimize processes. In this work, we hypothesized that a design of experiments (DoE) model would be able to support the optimization of the collagen-chitosan-fucoidan cryogel manufacturing. Therefore, the parameters temperature (A), collagen concentration (B), and fucoidan concentration (C) were carefully considered to be applied to the Box-Behnken design (three factors and three levels). Data obtained on rheological oscillatory measurements, as well as on the evaluation of antioxidant concentration and adenosine triphosphate (ATP) concentration, showed that fucoidan concentration could significantly influence collagen-chitosan-fucoidan cryogel formation, creating a stable internal polymeric network promoted by ionic crosslinking bonds. Additionally, the effect of temperature significantly contributed to rheological oscillatory properties. Overall, the condition that allowed us to have better results, from an optimization point of view according to the DoE, were the gels produced at -80 degrees C and composed of 5% of collagen, 3% of chitosan, and 10% fucoidan. Therefore, the proposed DoE model was considered suitable for predicting the best parameter combinations needed to develop these cryogels.

Abstract
Disasters, both natural and man-made, are extreme and complex events with consequences that translate into a loss of life and/or destruction of properties. The advances in IT and Big Data analysis represent an opportunity for the development of resilient environments once the application of analytical methods allows extracting information from a significant amount of data, optimizing the decision-making processes. This research aims to apply the CRISP-DM methodology to extract information about incidents that occurred in the city of Lisbon with emphasis on occurrences that affected buildings, constituting a tool to assist in the management of the city. Through this research, it was verified that there are temporal and spatial patterns of occurrences that affected the city of Lisbon, with some types of occurrences having a higher incidence in certain periods of the year, such as floods and collapses that occur when there are high levels of precipitation. On the other hand, it was verified that the downtown area of the city is the area most affected by occurrences. Finally, machine learning models were applied to the data and the predictive model Random Forest obtained the best result with an accuracy of 58%.