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Rationale: Pregnant women are especially vulnerable to mental health problems, including stress, anxiety, and depression. This risk has been increased during the COVID-19 pandemic and differences in psychological symptoms in pregnancy and postpartum before and during COVID-19 exists. Mental health problems can have adverse effects on both the woman, and the neonate, including miscarriages, premature births, low birth weight, and higher rates of cesarean sections and instrument-assisted deliveries. Aim: To evaluate the prevalence of psychological symptoms of pregnant women before and during the COVID-19 pandemic. Method: A cross-sectional study was carried out to assess the psychological profile in a sample of pregnant women selected before the pandemic and a sample of women studied during the first wave of the pandemic. A total of 122 women were selected prior to the pandemic and 181 women during the COVID-19 outbreak. The Symptom Checklist-90 Revised (SCL-90-R) was used to assess depression and anxiety during pregnancy and in the postpartum period. Results: The prevalence of symptoms of depression and anxiety during pregnancy were higher in the sample of women studied during the pandemic (SCL-90-R: M = 54.6 vs. 42.6 and M = 62.6 vs. 51.7 respectively). In the postpartum the difference between both samples of women was even higher for depression and anxiety (SCL-90-R: M = 50.4 vs. 35.0 and M = 51.3 vs. 36.0 respectively). Being a pregnant woman at the COVID-19 outbreak was directly associated with a higher score of depression symptoms (aOR = 8.67, 95% CI [3.26, 23.02], p <.001). Anxiety during childbirth was more frequently reported by women before the pandemic (aOR = 5.13, 95% CI [2.53, 10.44], p <.001). The variable stage (before /during pandemic) was also associated with having a clinical SCL-90-R score above 70 (aOR = 7.61, 95% CI [2.7, 21.47], p <.001). Conclusion: During the pandemic, pregnancy and postpartum were associated with the presence of anxiety and depression. The main factor associated with postpartum depression was maternal age. These finding highlights the vulnerability of pregnant in front of the pandemic, in which psychological health and mental well-being are affected, not only during pregnancy, but also in the postpartum. [ABSTRACT FROM AUTHOR]

Recent research indicates that information and communication technologies (ICTs) can support social participation in the planning, design and maintenance of public spaces (PDMPS), specifically to create comprehensive knowledge among different stakeholders. However, critics point out that the use of ICTs by planners and decision-makers often ignores the needs of local residents. For this research, we inquired how ICTs can support social participation in PDMPS. Our case study combines a literature review and 21 semi-structured interviews with government officials, non-governmental organisations, academics and architecture/urban planning consultancy companies in Mexico to understand how different stakeholders use ICTs to improve the quality of public spaces. We developed an approach that facilitates the analysis of ICT aspects related to hardware and software supporting social participation in PDMPS. The findings show that Mexico has a base of digital tools requiring technical capacities and spatial literacy at various stages of PDMPS, and ICTs are seen as an opportunity to engage with residents. However, in practice, our interviewees mentioned that ICTs are rarely implemented to support participatory processes due to high costs, a lack of political support and the insufficient technical expertise of technical staff to engage with residents using ICTs. The paper closes with recommendations and suggestions for future research on how ICTs can better support participatory processes in PDMPS. [ABSTRACT FROM AUTHOR]

Nowadays the European Universities are worried about how to adapt higher education to the new European Higher Education Area, as proposed in the Bologna Magna Charta Universitatum of 1998, and signed by 32 European Education Ministers. One of the key points in this higher education reform was the introduction of new Master's level curricula. These Masters will look for the professional specialization and they will have to be adapted to the specific skills demanded by the society. This paper presents the results from a cooperation project--funded by the AECI (International Cooperation Spanish Agency) and developed between the University of Seville (Spain) and the Catholic University of Asuncion (Paraguay)--by about the planning of a new master's level curriculum in digital signal processing (DSP) area, taking into account the Bologna principles and the conclusions obtained by the Career Space Consortium. A scientific method from social science, known as concept mapping techniques, was used to perform this planning. Basically, the idea of concept mapping consists of compiling the opinion from actors belonging to different environments (lecturers, researchers, workers, businessmen, etc.) related to DSP applications. The compiled data were statistically computed to cluster the opinions of the different agents. The result will be a master's level curricula adapted to the environment requirements and the social settings.

This article proposes a novel application method, inverse application of artificial intelligence (IAAI) for the control of power electronic converter systems. The proposed method can give the desired control coefficients/references in a simple way because, compared to conventional methods, IAAI only relies on a data-driven process with no need for an optimization process or substantial derivations. Noting that the IAAI approach uses artificial intelligence to provide feasible coefficients/references for the power converter control, rather than building a new controller. After illustrating the IAAI concept, a conventional application method of artificial neural network is discussed, an optimization-based design. Then, a two-source-converter microgrid case is studied to choose the best droop coefficients via the optimization-based approach. After that, the proposed IAAI method is employed for the same microgrid case to quickly find good droop coefficients. Furthermore, the IAAI method is applied to a modular multilevel converter (MMC) case, extending the MMC operation region under unbalanced grid faults. In the MMC case, both simulation and experimental online tests validate the operation, feasibility, and practicality of IAAI. [ABSTRACT FROM AUTHOR]