Abstract
<p>Atmospheric new particle formation (NPF) generates secondary aerosol particles into the lower atmosphere via gas-to-particle phase transition. Secondary aerosol particles dominate the total particle number concentration and are an important source for cloud condensation nuclei <sup>[1]</sup>. NPF typically begins with clustering among gaseous molecules. Once the newly formed clusters attain a size larger than the critical cluster size (~1.5 nm), their growth to larger sizes is energetically favoured and eventually they become nanoparticles <sup>[2]</sup>. NPF is often observed with the participation of air ions <sup>[3]</sup> and sometimes is induced by ions <sup>[4]</sup>. Air ions are a constituent of atmospheric electricity. The presence of the Earth-atmosphere electric field poses an electrical force on air ions. The earth-atmosphere electric field exhibits variability at different time scales under fair-weather conditions <sup>[5]</sup>. It is therefore interesting to understand whether the Earth-atmosphere electric field influences atmospheric new particle formation.</p> <p>We analysed the Earth-atmosphere electric field together with the number size distribution data of air ions and aerosol particles under fair-weather conditions measured at Hyytiälä SMEAR II station in Southern Finland <sup>[6]</sup>. The electric field were measured by two Campbell CS 110 field mills in parallel. Air ion data were obtained with a Balance Scanning Mobility Analyser (BSMA) and a Neutral and Air Ion Spectrometer (NAIS), and aerosol particle data with a Differential Mobility Particle Sizer (DMPS). We used condensation Sinks (CS) derived from the DMPS measurement, air temperature, relative humidity, wind speed, global radiation as well as brightness derived from the global radiation measurement to assist the analysis. The measured earth-atmosphere electric field on NPF days was higher than on non-NPF days. We found that under low CS conditions, the electric field can enhance the formation of 1.7-3 nm air ions, but the concentration of 1.7-3 nm ions decreased with an increasing electric field under high CS conditions.</p> <p>References:</p> <p>[1]       Kerminen V.-M. et al., Environ. Res. Lett. <strong>2018</strong>, 13, 103003.</p> <p>[2]       Kulmala M. et al., Science <strong>2013</strong>, 339, 943-946.</p> <p>[3]       Manninen H. E. et al., Atmos. Chem. Phys. <strong>2010</strong>, 10, 7907-7927.</p> <p>[4]       Jokinen T. et al., Science Advances <strong>2018</strong>, 4, eaat9744.</p> <p>[5]       Bennett A. J., Harrison R. G., Journal of Physics: Conference Series <strong>2008</strong>, 142, 012046.</p> <p>[6]       Hari P., Kulmala M., Boreal Environ. Res. <strong>2005</strong>, 10, 315-322.</p>

Abstract
The link prediction task has found numerous applications in real-world scenarios. However, in most of the cases like interactions, purchases, mobility, etc., links can re-occur again and again across time. As a result, the data being generated is excessively large to handle, associated with the complexity and sparsity of networks. Therefore, we propose a very fast, memory-less, and dynamic sampling-based method for predicting recurring links for a successive future point in time. This method works by biasing the links exponentially based on their time of occurrence, frequency, and stability. To evaluate the efficiency of our method, we carried out rigorous experiments with massive real-world graph streams. Our empirical results show that the proposed method outperforms the state-of-the-art method for recurring links prediction. Additionally, we also empirically analyzed the evolution of links with the perspective of multi-graph topology and their recurrence probability over time.

Abstract
<p>Although the Earth system is described to react relatively abruptly to present anthropogenic forcings, the notion of abruptness remains questionable as it refers to a time scale that is difficult to constrain properly. Recognizing this issue, the tipping elements as listed in Lenton et al. (2008) rely on long-term observations under controlled conditions, which enabled the associated tipping points to be identified. For example, there is evidence nowadays that if the rate of deforestation from forest fires and the climate change does not decrease, the Amazonian forest will reach a tipping point towards savanna (Nobre, 2019), which would impact the regional and global climate systems as well as various other ecosystems, directly or indirectly (Magalhães et al., 2020). However, if the present tipping elements, which are now evidenced, are mostly related to the present climate change and thus directly or indirectly related to anthropogenic forcing, their interpretation must still rely on former cases detected in the past, and especially from studies of abrupt climatic transitions evidenced in paleoclimate proxy records. Moreover, recent studies of past changes have shown that addressing abrupt transitions in the past raises the issue of data quality of individual records, including the precision of the time scale and the quantification of associated uncertainties. Investigating past abrupt transitions and the mechanisms involved requires the best data quality possible. This can be a serious limitation when considering the sparse spatial coverage of high resolution paleo-records where dating is critical and corresponding errors often challenging to control. In theory, this would therefore almost limit our investigations to ice-core records of the last climate cycle, because they offer the best possible time resolution. However, evidence shows that abrupt transitions can also be identified in deeper time with lower resolution records, but still revealing changes or transitions that have impacted the dynamics of the Earth system globally. TiPES Work Package 1 will address these issues and collect paleorecords permitting to evidence the temporal behavior of tipping elements in past climates, including several examples.</p> <p>Lenton T. et al. (2008). PNAS 105, 1786-1793.</p> <p>Nobre C. (2019). Nature 574, 455.</p> <p>Magalhães N.d. et al. (2020). Sci. Rep. 16914 (2019) doi:10.1038/s41598-019-53284-1</p> <p>This work is performed under the TiPES project funded by the European Union’s Horizon 2020 research and innovation program under grant agreement # 820970 <https://tipes.sites.ku.dk/></p>

Abstract
Grade prediction has been for a long time a subject that interests both teachers and researchers. Before the digital age this type of predictions was something nearly impossible to achieve. With the increasing integration of Learning Management Systems in education, grade prediction seems to have become a viable option. The general adoption of this type of systems brings to the research area a database known as “registry”, or more simply known as logged data. Using this new source of information several attempts regarding the prediction of student grades have been proposed. The methodology proposed in this study is capable of, analyzing student online behavior, using the information collected by the Moodle system and making a prediction on what the final grade of the student will be, at any point in the semester. Our novel approach uses the gathered information to examine the academic path of the student in order to determine an interaction pattern, then it tries to establish a link with other, present or past, known successful paths. Making this comparison, the model can automatically determine if a student is going to fail or pass the course, which then would leave a space for the teacher or the student to circumvent the situation. Our results show that the system is not only viable, as it is also robust to make prediction at an early stage in the course.

Abstract
Purpose In the vast majority of the individual robot installations, the robot arm is just one piece of a complex puzzle of components, such as grippers, jigs or external axis, that together compose an industrial robotic cell. The success of such installations is very dependent not only on the selection of such components but also on the layout and design of the final robotic cell, which are the main tasks of the system integrators. Consequently, successful robot installations are often empirical tasks owing to the high number of experimental combinations that could lead to exhaustive and time-consuming testing approaches. Design/methodology/approach A newly developed optimized technique to deal with automatic planning and design of robotic systems is proposed and tested in this paper. Findings The application of a genetic-based algorithm achieved optimal results in short time frames and improved the design of robotic work cells. Here, the authors show that a multi-layer optimization approach, which can be validated using a robotic tool, is able to help with the design of robotic systems. Originality/value To date, robotic solutions lack flexibility to cope with the demanding industrial environments. The results presented here formalize a new flexible and modular approach, which can provide optimal solutions throughout the different stages of design and execution control of any work cell.

Abstract
Background Decision-making in palliative care can be complex due to the uncertain prognosis and general fear surrounding decisions. Decision-making in palliative care may be influenced by spiritual and cultural beliefs or values. Determinants of the decision-making process are not completely understood, and spirituality is essential for coping with illness. Thus, this study aims to explore the influence of spirituality on the perception of healthcare decision-making in palliative care outpatients. Methods A cross-sectional study was developed. A battery of tests was administered to 95 palliative outpatients, namely: sociodemographic questionnaire (SQ), Decisional Conflict Scale (DCS), Functional Assessment of Chronic Illness Therapy-Spiritual Well-Being scale (FACIT-Sp), and a semi-structured interview (SSI) to study one's perception of spirituality and autonomy in decision-making. Statistical analyses involved descriptive statistics for SQ and SSI. The Mann-Whitney test was used to compare scale scores between groups and correlations were used for all scales and subscales. The analysis of patients' definitions of spirituality was based on the interpretative phenomenological process. Results Spiritual wellbeing significantly correlated with greater levels of physical, emotional and functional wellbeing and a better quality of life. Greater spiritual wellbeing was associated with less decisional conflict, decreased uncertainty, a feeling of being more informed and supported and greater satisfaction with one's decision. Most patients successfully implemented their decision and identified themselves as capable of early decision-making. Patients who were able to implement their decision presented lower decisional conflict and higher levels of spiritual wellbeing and quality of life. Within the 16 themes identified, spirituality was mostly described through family. Patients who had received spiritual care displayed better scores of spiritual wellbeing, quality of life and exhibited less decisional conflict. Patients considered spirituality during illness important and believed that the need to receive spiritual support and specialised care could enable decision-making when taking into consideration ones' values and beliefs. Conclusion The impact of spiritual wellbeing on decision-making is evident. Spirituality is a key component of overall wellbeing and it assumes multidimensional and unique functions. Individualised care that promotes engagement in decision-making and considers patients' spiritual needs is essential for promoting patient empowerment, autonomy and dignity.