Abstract
Organizations are more frequently turning towards robotic process automation (RPA) as a solu-tion for employees to focus on higher complexity and more valuable tasks while delegating routine, monotonous and rule-based tasks to their digital colleagues. These software robots can handle various rule-based, digital, repetitive tasks. However, currently available process identi-fication methods must be qualified to select suitable automation processes accurately. Wrong process selection and failed attempts are often the origin of process automation's bad reputation within organizations and often result in the avoidance of this technology. As a result, in this research, a method for selecting processes for automation combining two multi-criteria decision -making techniques, 'Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), will be proposed, demonstrated, and evaluated. This study follows the Design Science Research Methodology (DSRM) and applies the proposed method for selecting processes for automation to a real-life scenario. The result will be a method to support the proper selection of business processes for automation, increasing the success of implementing RPA tools in an organization.

Abstract
Business Process Automation has been gaining increasing importance in the management of companies and organizations since it reduces the time needed to carry out routine tasks, freeing employees for other more creative and exciting things. The use of process automation seems to be a growing trend in the business's operational restructuring, combined with digital transformation. It can be applied in the most varied business areas. Organizations from any sector of activity can also adopt it. Given these benefits, the granted success in transforming business processes would be expected. However, 30 to 50% of automation initiatives with Robotic Process Automation technology fail. In this work, a set of guidelines will be proposed that will constitute, after validation, a framework capable of guiding organizations, with a focus on SMEs, in the procedure of automating their processes, thus obtaining the maximum return of this transformation.

Abstract
In recent years, the research community has raised serious questions about the replicability and reproducibility of scientific work. In particular, since many studies include some kind of computing work, these are also technological challenges, not only in computer science but in most research domains. Replicability and reproducibility are not easy to achieve, not only because researchers have diverse proficiency in computing technologies, but also because of the variety of computational environments that can be used. Indeed, it is challenging to recreate the same environment using the same frameworks, code, programming languages, dependencies, and so on. In this work, we propose a vision for an Integrated Development Environment allowing the creation, configuration, execution, packaging, and sharing of scientific computational experiments. Such a framework should allow researchers to easily set the code and data used and define the programming languages, code, dependencies, databases, or commands to execute to achieve consistent results for each experiment. With this work, we intend to aid researchers by integrating into the same platform all the stages of the design, execution, and analysis of a computational experiment.

Abstract
Radon concentration was continuously monitored in a horizontal dead-end gallery near Vyhne (Central Slovakia) from October 2005 to April 2008. Hourly average of radon varied from 2800 to 10 500 Bq/m(3). Temporal variation of radon, which contains periodic and non-periodic signals, spans variation of annual to diurnal scale. Time series of radon were analyzed together with meteorological parameters. The annual variation of radon seems to be connected with the annual variation of atmospheric pressure. The amplitude and shape of diurnal variation of radon changed during the year and is correlated with corresponding changes in the daily amplitude of atmospheric pressure.

Abstract
<p>A novel ionisation detector, previously deployed on meteorological radiosonde flights, has demonstrated responsivity to X-rays and gamma radiation, and additionally, is thought to be sensitive to ionising radiation from cosmic rays. The PiN detector, composed of a 1x1x0.8 cm<sup>3 </sup>CsI(Tl) microscintillator coupled to a PiN photodiode, was deployed on the NRP Sagres sailing vessel on a cruise in the Atlantic between Portugal and the Azores in 2021. The instrument can determine both the count rate and energy of incoming ionising radiation particles.</p><p>The instrument was operational during the voyage in November 2021 when a coronal mass ejection event induced a sudden decrease in the observed cosmic ray intensity, known as a Forbush decrease. We present data recorded by the ionisation detector during this period, to characterise the instrument’s ability to detect cosmic ray events, and we compare the performance with neutron monitoring stations Oulu in Finland, and Dourbes in Belgium. As the PiN detector provides spectral and count rate data, it is possible to group events by their energy, and investigate the count rates of specific energy regimes. This approach is useful as many sources – including high and low energy ionising radiation from cosmic rays – contribute to the background energy spectrum. As a result, more meaningful comparisons and relationships can be established with the neutron monitoring stations.</p>

Abstract
Greenland ice core records display abrupt transitions, designated as Dansgaard-Oeschger (DO) events, characterised by episodes of rapid warming (typically decades) followed by a slower cooling. The identification of abrupt transitions is hindered by the typical low resolution and small size of paleoclimate records, and their significant temporal variability. Furthermore, the amplitude and duration of the DO events varies substantially along the last glacial period, which further hinders the objective identification of abrupt transitions from ice core records Automatic, purely data-driven methods, have the potential to foster the identification of abrupt transitions in palaeoclimate time series in an objective way, complementing the traditional identification of transitions by visual inspection of the time series.In this study we apply an algorithmic time series method, the Matrix Profile approach, to the analysis of the NGRIP Greenland ice core record, focusing on:- the ability of the method to retrieve in an automatic way abrupt transitions, by comparing the anomalies identified by the matrix profile method with the expert-based identification of DO events;- the characterisation of DO events, by classifying DO events in terms of shape and identifying events with similar warming/cooling temporal patternThe results for the NGRIP time series show that the matrix profile approach struggles to retrieve all the abrupt transitions that are identified by experts as DO events, the main limitation arising from the diversity in length of DO events and the method’s dependence on fixed-size sub-sequences within the time series. However, the matrix profile method is able to characterise the similarity of shape patterns between DO events in an objective and consistent way.