What is the impact of Information Systems on democracy promotion and the role in decision-making process

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementThis study aims to contribute to a better understanding of modern democracy and how democracy can be shaped by information systems solutions. We discuss the role of information systems and social media in democratic activities and how information systems can be a part of core democratic processes and contribute to finding solutions for some of the problems democracies face today. The main question being: how is democracy fostered by the introduction of information systems and the existing information systems platforms today? Several common problems of democracies will be identified, analyzed and paired with relevant information systems platforms or solutions resulting in a conceptual framework that nations can use to improve their democratic processes. Areas identified as relevant for the study are direct democracy using existing technological solutions, collaborative democracy, which would allow citizens to increase participation in the creation of laws, the allocation of budgets and online voting. Although it might not be possible to provide an exhaustive listing of all existing solution, due to the rapidly evolving nature of the information systems field, several existing solutions already provide interesting opportunities for the improvement of current democratic processes and if there was a wider adoption of these technologies it would improve the participation of citizens and reduce the increasing percentage of alienated citizens that abstain from taking part in the democratic process of their countries

Pediocins: The bacteriocins of Pediococci. Sources, production, properties and applications

Class IIa bacteriocins from lactic acid bacteria are small, cationic proteins with antilisterial activity. Within this class, the pediocins are those bacteriocins that share a highly conserved hydrophilic and charged N-terminal part harboring the consensus sequence -YGNGV- and a more variable hydrophobic and/or amphiphilic C-terminal part. Several pediocins have been isolated and characterized. Despite the structural similarities, their molecular weight varies, as well as their spectrum of antimicrobial activity. They exhibit important technological properties, e.g. thermostability and retaining of activity at a wide pH range, which along with the bactericidal action against Gram-positive food spoilage and pathogenic bacteria, make them an important class of biopreservatives. Much new information regarding the pediocins has emerged during the last years. In this review, we summarize and discuss all the available information regarding the sources of pediocins, the characteristics of their biosynthesis and production in fermentation systems, the characteristics of the known pediocin molecules, and their antibacterial action. The advances made by genetic engineering in improving the features of pediocins are also discussed, as well as their perspectives for future applications

Which technology to which challenge in democratic governance? An approach using design science research

Anastasiadou, M., Santos, V., & Montargil, F. (2021). Which technology to which challenge in democratic governance? An approach using design science research. Transforming Government: People, Process and Policy, 15(4), 512-531. https://doi.org/10.1108/TG-03-2020-0045Purpose: Information systems (IS) play an important role in contemporary society, but critical questions remain on their potential use and impact on democracy. This study aims to contribute to the discussion on which technology can be adequate to which major challenge of democratic governance, through the identification and pairing of challenges of democratic governance with specific information technologies with the potential to be used in applications related to this challenge. This perspective can be considered positioned in the confluence between IS, political science and public administration. Design/methodology/approach: Design science research, a research approach in IS, was used. The suggestion of a conceptual framework with pairs of challenges in democratic governance and information technologies was initially developed. In a subsequent phase, this framework was discussed and assessed through interviews with a panel of selected experts in e-government and IS, reaching a revised conceptual framework. Findings: Results suggest that the conceptual pairing of challenges in democratic governance with IS’s solutions such as artificial intelligence, systems integration or blockchain technologies, for instance, if used in a critical, transparent and accountable way, can play a role in capacitating the delivery of better public services and contribute to encouraging citizen trust and political participation. These results may contribute to open a methodological agenda dedicated to the selection of adequate IS resources to address specific challenges of democratic governance, as well as to help in the development of public policies in the area. Originality/value: Previous studies on digital government offer important insights on the impact of information and communication technologies-enabled public governance tools for government openness, public service efficiency and user-friendliness, and for citizen political participation and societal mobilization. But the literature still lacks a systematic conceptual framework mapping and assessing the role of distinctive IS instruments in democratic challenge-solving and specifying functional relationships between specific technology and democratic outcomes. This paper aims to contribute to filling this analytical gap.authorsversionpublishe

Evaluating Energy Performance Certificate Data with Data Science

Anastasiadou, M., Santos, V., & Dias, M. S. (2021). Evaluating Energy Performance Certificate Data with Data Science. In 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET) (pp. 1-5). IEEE. https://doi.org/10.1109/ICECET52533.2021.9698806The related problems of improving existing buildings' energy performance, reducing energy consumption, and improving indoor comfort and their many consequences are well known. Considering increasing urbanization and climate change, governments define strategies to enhance and measure buildings' energy performance and energy efficiency. This work aims to contribute to the improvement of buildings' characteristics by conducting a thorough systematic literature review and adopting a data science approach to these problems, presenting initial results with an open-access energy performance certificate dataset from the Lombardy Region, in Italy. We provide a pre-processing method to the data, applicable for future research, aiming to address challenges such as automatic classification of existing buildings' energy performance certification, and predicting energy-efficient retrofit measures, using machine learning techniques. The analysis of this dataset is challenging because of the high variability and dimensionality of this dataset. For this purpose, a robust iterative process was developed. First, the data dimensionality was reduced with Pearson Correlation to find the best set of variables against the non-renewable global energy performance index (EPgl, nren). Then, the outliers were handled by utilizing Box Plot and Isolation Forest algorithms. The main contribution is to inform private and public building sectors on dealing with high dimensional data to achieve enhanced energy performance and predict energy-efficient retrofit measures to improve their energy performance.authorsversionpublishe

a model to improve the evaluation and selection of public contest candidates in the Police Force

Goncalves, M. B., Anastasiadou, M., & Santos, V. (2022). AI and public contests: a model to improve the evaluation and selection of public contest candidates in the Police Force. Transforming Government: People, Process and Policy, 16(4), 22. https://doi.org/10.1108/TG-05-2022-0078 ---- Bailao Goncalves, M., Anastasiadou, M., & Santos, V. (2022). AI and public contests: a model to improve the evaluation and selection of public contest candidates in the Police Force. Transforming Government: People, Process and Policy. https://doi.org/10.1108/TG-05-2022-0078Abstract Purpose The number of candidates applying to public contests (PC) is increasing compared to the number of human resources employees required for selecting them for the Police Force (PF). This work intends to perceive how those public institutions can evaluate and select their candidates efficiently during the different phases of the recruitment process. To achieve this purpose, artificial intelligence (AI) was studied. This paper aims to focus on analysing the AI technologies most used and appropriate to the PF as a complementary recruitment strategy of the National Criminal Investigation police agency of Portugal – Polícia Judiciária. Design/methodology/approach Using design science research as a methodological approach, the authors suggest a theoretical framework in pair with the segmentation of the candidates and comprehend the most important facts facing public institutions regarding the usage of AI technologies to make decisions about evaluating and selecting candidates. Following the preferred reporting items for systematic reviews and meta-analyses methodology guidelines, a systematic literature review and meta-analyses method was adopted to identify how the usage and exploitation of transparent AI positively impact the recruitment process of a public institution, resulting in an analysis of 34 papers between 2017 and 2021. Findings Results suggest that the conceptual pairing of evaluation and selection problems of candidates who apply to PC with applicable AI technology such as K-means, hierarchical clustering, artificial neural network and convolutional neural network algorithms can support the recruitment process and could help reduce the workload in the entire process while maintaining the standard of responsibility. The combination of AI and human decision-making is a fair, objective and unbiased process emphasising a decision-making process free of nepotism and favouritism when carefully developed. Innovative and modern as a category, group the statements that emphasise the innovative and contemporary nature of the process. Research limitations/implications There are two main limitations in this study that should be considered. Firstly, the difficulty regarding the timetable, privacy and legal issues associated with public institutions. Secondly, a small group of experts served as the validation group for the new framework. Individual semi-structured interviews were conducted to alleviate this constraint. They provide additional insights into an interviewee’s opinions and beliefs. Social implications Ensure that the system is fair, transparent and facilitates their application process. Originality/value The main contribution is the AI-based theoretical framework, applicable within the analysis of literature papers, focusing on the problem of how the institutions can gain insights about their candidates while profiling them, how to obtain more accurate information from the interview phase and how to reach a more rigorous assessment of their emotional intelligence providing a better alignment of moral values. This work aims to improve the decision-making process of a PF institution recruiter by turning it into a more automated and evidence-based decision when recruiting an adequate candidate for the job vacancy.authorsversionpublishe

Machine Learning Techniques Focusing on the Energy Performance of Buildings: A Dimensions and Methods Analysis

109 “Consumo SMART”. This work is partially funded by national funds through FCT—Foundation for Science and Technology, I.P., under the project FCT UIDB/04466/2020.The problem of energy consumption and the importance of improving existing buildings’ energy performance are notorious. This work aims to contribute to this improvement by identifying the latest and most appropriate machine learning or statistical techniques, which analyze this problem by looking at large quantities of building energy performance certification data and other data sources. PRISMA, a well-established systematic literature review and meta-analysis method, was used to detect specific factors that influence the energy performance of buildings, resulting in an analysis of 35 papers published between 2016 and April 2021, creating a baseline for further inquiry. Through this systematic literature review and bibliometric analysis, machine learning and statistical approaches primarily based on building energy certification data were identified and analyzed in two groups: (1) automatic evaluation of buildings’ energy performance and, (2) prediction of energy-efficient retrofit measures. The main contribution of our study is a conceptual and theoretical framework applicable in the analysis of the energy performance of buildings with intelligent computational methods. With our framework, the reader can understand which approaches are most used and more appropriate for analyzing the energy performance of different types of buildings, discussing the dimensions that are better used in such approaches.publishersversionpublishe

Haematoma caused by bone marrow aspiration and trephine biopsy

We report a case of a bone marrow aspiration and trephine biopsy (BMATB) associated haematoma in an 85-years old male without any predisposing risk factors. Six days after BMATB, he suffered from a massive thigh and buttock haematoma and a fall in haematocrit. It is important to know that BMATB can have complications aiding early recognition and therapy

Immune and Epstein-Barr virus gene expression in cerebrospinal fluid and peripheral blood mononuclear cells from patients with relapsing-remitting multiple sclerosis

Background: Gene expression analyses in paired cerebrospinal fluid (CSF) and peripheral blood mononuclear cells (PBMC) from patients with multiple sclerosis (MS) are restrained by the low RNA amounts from CSF cells and low expression levels of certain genes. Here, we applied a Taqman-based pre-amplification real-time reverse-transcription polymerase chain reaction (RT-PCR) (PreAmp RT-PCR) to cDNA from CSF cells and PBMC of MS patients and analyzed multiple genes related to immune system function and genes expressed by Epstein-Barr virus (EBV), a herpesvirus showing strong association with MS. Using this enhanced RT-PCR method, we aimed at the following: (1) identifying gene signatures potentially useful for patient stratification, (2) understanding whether EBV infection is perturbed in CSF and/or blood, and (3) finding a link between immune and EBV infection status. Methods: Thirty-one therapy-free patients with relapsing-remitting MS were included in the study. Paired CSF cells and PBMC were collected and expression of 41 immune-related cellular genes and 7 EBV genes associated with latent or lytic viral infection were determined by PreAmp RT-PCR. Clinical, radiological, CSF, and gene expression data were analyzed using univariate and multivariate (cluster analysis, factor analysis) statistical approaches. Results: Several immune-related genes were differentially expressed between CSF cells and PBMC from the whole MS cohort. By univariate analysis, no or only minor differences in gene expression were found associated with sex, clinical, or radiological condition. Cluster analysis on CSF gene expression data grouped patients into three clusters; clusters 1 and 2 differed by expression of genes that are related mainly to innate immunity, irrespective of sex and disease characteristics. By factor analysis, two factors grouping genes involved in antiviral immunity and immune regulation, respectively, accurately discriminated cluster 1 and cluster 2 patients. Despite the use of an enhanced RT-PCR method, EBV transcripts were detected in a minority of patients (5 of 31), with evidence of viral latency activation in CSF cells or PBMC and of lytic infection in one patient with active disease only. Conclusions: Analysis of multiple cellular and EBV genes in paired CSF cell and PBMC samples using PreAmp RT-PCR may yield new information on the complex interplay between biological processes underlying MS and help in biomarker identification

MiR-200c-3p maintains stemness and proliferative potential in adipose-derived stem cells by counteracting senescence mechanisms

Adipose-derived mesenchymal stem cells (ASCs) are promising therapeutic tools in regenerative medicine because they possess self-renewal, differentiation and immunomodulatory capacities. After isolation, ASCs are passaged multiple times in vitro passages to obtain a sufficient amount of cells for clinical applications. During this time-consuming procedure, ASCs become senescent and less proliferative, compromising their clinical efficacy. Here, we sought to investigate how in vitro passages impact ASC proliferation/senescence and expression of immune regulatory proteins. MicroRNAs are pivotal regulators of ASC physiology. Particularly, miR-200c is known to maintain pluripotency and targets the immune checkpoint Programmed death-ligand 1 (PD-L1). We therefore investigated its involvement in these critical characteristics of ASCs during in vitro passages. We found that when transiently expressed, miR-200c-3p promotes proliferation, maintains stemness, and contrasts senescence in late passaged ASCs. Additionally, this miRNA modulates PD-L1 and Indoleamine 2,3-Dioxygenase (IDO1) expression, thus most likely interfering with the immunoregulatory capacity of ASCs. Based on our results, we suggest that expression of miR-200c-3p may prime ASC towards a self-renewing phenotype by improving their in vitro expansion. Contrarily, its inhibition is associated with senescence, reduced proliferation and induction of immune regulators. Our data underline the potential use of miR-200c-3p as a switch for ASCs reprogramming and their clinical application

Graph Theoretical Characteristics of EEG-Based Functional Brain Networks in Patients With Epilepsy: The Effect of Reference Choice and Volume Conduction

It is well-established that both volume conduction and the choice of recording reference (montage) affect the correlation measures obtained from scalp EEG, both in the time and frequency domains. As a result, a number of correlation measures have been proposed aiming to reduce these effects. In our previous work, we have showed that scalp-EEG based functional brain networks in patients with epilepsy exhibit clear periodic patterns at different time scales and that these patterns are strongly correlated to seizure onset, particularly at shorter time scales (around 3 and 5 h), which has important clinical implications. In the present work, we use the same long-duration clinical scalp EEG data (multiple days) to investigate the extent to which the aforementioned results are affected by the choice of reference choice and correlation measure, by considering several widely used montages as well as correlation metrics that are differentially sensitive to the effects of volume conduction. Specifically, we compare two standard and commonly used linear correlation measures, cross-correlation in the time domain, and coherence in the frequency domain, with measures that account for zero-lag correlations: corrected cross-correlation, imaginary coherence, phase lag index, and weighted phase lag index. We show that the graphs constructed with corrected cross-correlation and WPLI are more stable across different choices of reference. Also, we demonstrate that all the examined correlation measures revealed similar periodic patterns in the obtained graph measures when the bipolar and common reference (Cz) montage were used. This includes circadian-related periodicities (e.g., a clear increase in connectivity during sleep periods as compared to awake periods), as well as periodicities at shorter time scales (around 3 and 5 h). On the other hand, these results were affected to a large degree when the average reference montage was used in combination with standard cross-correlation, coherence, imaginary coherence, and PLI, which is likely due to the low number of electrodes and inadequate electrode coverage of the scalp. Finally, we demonstrate that the correlation between seizure onset and the brain network periodicities is preserved when corrected cross-correlation and WPLI were used for all the examined montages. This suggests that, even in the standard clinical setting of EEG recording in epilepsy where only a limited number of scalp EEG measurements are available, graph-theoretic quantification of periodic patterns using appropriate montage, and correlation measures corrected for volume conduction provides useful insights into seizure onset