Abstract
Antarctic deserts, such as the McMurdo Dry Valleys (MDV), represent extremely cold and dry environments. Consequently, MDV are suitable for studying the environment limits on the cycling of key elements that are necessary for life, like nitrogen. The spatial distribution and biogeochemical drivers of nitrogen-cycling pathways remain elusive in the Antarctic deserts because most studies focus on specific nitrogen-cycling genes and/or organisms. In this study, we analyzed metagenome and relevant environmental data of 32 MDV soils to generate a complete picture of the nitrogen-cycling potential in MDV microbial communities and advance our knowledge of the complexity and distribution of nitrogen biogeochemistry in these harsh environments. We found evidence of nitrogen-cycling genes potentially capable of fully oxidizing and reducing molecular nitrogen, despite the inhospitable conditions of MDV. Strong positive correlations were identified between genes involved in nitrogen cycling. Clear relationships between nitrogen-cycling pathways and environmental parameters also indicate abiotic and biotic variables, like pH, water availability, and biological complexity that collectively impose limits on the distribution of nitrogen-cycling genes. Accordingly, the spatial distribution of nitrogen-cycling genes was more concentrated near the lakes and glaciers. Association rules revealed non-linear correlations between complex combinations of environmental variables and nitrogen-cycling genes. Association rules for the presence of denitrification genes presented a distinct combination of environmental variables from the remaining nitrogen-cycling genes. This study contributes to an integrative picture of the nitrogen-cycling potential in MDV.

Abstract
The article aims to appraise the role of Corporate Social Responsibility (CSR) and innovation strategies as leverages of a company's financial performance. The theoretical and empirical statement of this link aims to reinforce the importance of these strategical options in both the managerial and the public policy domain. Shedding light on the economic return of these practices will help managers make better strategic decisions. Policy makers will also grasp the required evidence to encompass CSR in policy packages. To address the research question, data were collected from the Thomson Reuters Eikon Datastream covering the 1000 largest companies listed on the stock exchange worldwide. Thereafter, hierarchical linear regressions were performed to produce the econometric results. Two time frames (2015-2019) were compared to address time-space trends. Enrolling in CSR activities entails additional costs which can undermine the company's financial performance if not properly supported by public policies. Combining CSR and innovation appears to be the best strategy for companies seeking improvements in their financial performance while being socially responsible. The contribution of this study is threefold: first, the analysis covers the largest thousand firms in operation worldwide; secondly, the econometric results demonstrate that combining CSR with innovation positively impacts financial performance; and lastly, the time comparison evidences a positive but slow evolution in CSR adoption. The article provides an applied perspective, of use both for managers and policy makers, as to how they should approach and disseminate involvement in these types of activities.

Abstract
The paper describes the MetroPT data set, an outcome of a Predictive Maintenance project with an urban metro public transportation service in Porto, Portugal. The data was collected in 2022 to develop machine learning methods for online anomaly detection and failure prediction. Several analog sensor signals (pressure, temperature, current consumption), digital signals (control signals, discrete signals), and GPS information (latitude, longitude, and speed) provide a framework that can be easily used and help the development of new machine learning methods. This dataset contains some interesting characteristics and can be a good benchmark for predictive maintenance models.

Abstract
Time expressions embedded in text are important for many downstream tasks in NLP and IR. They have been, for example, utilized for timeline summarization, named entity recognition, temporal information retrieval, question answering and others. In this paper, we introduce a novel analytical approach to analyzing characteristics of time expressions in diachronic text collections. Based on a collection of news articles published over a 33-years' long time span, we investigate several aspects of time expressions with a focus on their interplay with publication dates of containing documents. We utilize a graph-based representation of temporal expressions to represent them through their co-occurring named entities. The proposed approach results in several observations that could be utilized in automatic systems that rely on processing temporal signals embedded in text. It could be also of importance for professionals (e.g., historians) who wish to understand fluctuations in collective memories and collective expectations based on large-scale, diachronic document collections. © 2022 ACM.

Abstract
The paper presents the Scratch4All project, co-financed by the Portuguese Social Innovation Mission Structure, namely the presentation of the methodology applied in the impact assessment of the first year of the project. A project that uses Scratch software to teach programming languages to fourth grade students (elementary school). Based on the defined indicators, on the characteristics of the beneficiaries and on the activities implemented, adequate measurement instruments were developed to verify the impacts on the target audience. Using secondary and primary sources, indicators were developed to measure the level of change that occurred in each project beneficiary, using quantitative tools. Regarding the software used, Scratch, it was possible to perceive that it enables greater group work skills, working on current social issues and/or technological profiles, fosters greater responsibility in the use of technologies, within the scope of digital citizenship, generates an increase in creativity and innovation, greater willingness to investigate and research different topics, greater communication and collaborative work. © 2022 IEEE Computer Society. All rights reserved.

Abstract
Hashtags are commonly used in social media communication not only to categorize conversations but particularly to raise attention and generate debate of certain topics. Hashtag activism is one of the areas that is gaining particular attention from academics and the overall society. The focus of this chapter is hashtag attributes. Particularly, it analyses and compares four hashtags related to violence against women that circulated on social networks during the COVID-19 pandemic: #16Days, #IsolatedNotAlone, #womensupportingwomen, and #NiUnaMenos. The chapter highlights important aspects to increase the effectiveness of communication with the use of hashtags.