Understanding peace through the world news

Peace is a principal dimension of well-being and is the way out of inequity and violence. Thus, its measurement has drawn the attention of researchers, policymakers, and peacekeepers. During the last years, novel digital data streams have drastically changed the research in this field. The current study exploits information extracted from a new digital database called Global Data on Events, Location, and Tone (GDELT) to capture peace through the Global Peace Index (GPI). Applying predictive machine learning models, we demonstrate that news media attention from GDELT can be used as a proxy for measuring GPI at a monthly level. Additionally, we use explainable AI techniques to obtain the most important variables that drive the predictions. This analysis highlights each country’s profile and provides explanations for the predictions, and particularly for the errors and the events that drive these errors. We believe that digital data exploited by researchers, policymakers, and peacekeepers, with data science tools as powerful as machine learning, could contribute to maximizing the societal benefits and minimizing the risks to peace.Peace is a principal dimension of well-being and is the way out of inequity and violence. Thus, its measurement has drawn the attention of researchers, policymakers, and peacekeepers. During the last years, novel digital data streams have drastically changed the research in this field. The current study exploits information extracted from a new digital database called Global Data on Events, Location, and Tone (GDELT) to capture peace through the Global Peace Index (GPI). Applying predictive machine learning models, we demonstrate that news media attention from GDELT can be used as a proxy for measuring GPI at a monthly level. Additionally, we use explainable AI techniques to obtain the most important variables that drive the predictions. This analysis highlights each country’s profile and provides explanations for the predictions, and particularly for the errors and the events that drive these errors. We believe that digital data exploited by researchers, policymakers, and peacekeepers, with data science tools as powerful as machine learning, could contribute to maximizing the societal benefits and minimizing the risks to peace

Microplanktic assemblages (sarcodines and alveolates) in the central and southeastern Aegean Sea (NE Mediterranean)

Spatial changes in the structure of microplanktic assemblages (sarcodines and alveolates) in the surface waters of coastal regions in the central and southeastern Aegean Sea were estimated during late summer to early autumn. Tintinnidae (Ciliophora), as well, Acanthometridae (Radiolaria) and Globiderinidae (Foraminifera) exhibited a higher abundance in the central Aegean Sea, compared to that in the more oligotrophic southeastern area of the same sea. Multivariate analysis revealed that the samples from the eastern Cretan Sea (South Aegean Sea) were highly distinguished from all the others and showed the highest densities of polycystines (Radiolaria), specifically of the families Thalassicollidae, Thalassosphaeridae, Sphaerozoidae (Collodaria), as well as of Actinommidae (Spumellaria) and Theoperidae (Nassellaria). In addition, the families Ceratiaceae and Prorocentraceae (Dinoflagellata) were more abundant in the southeastern than in the central Aegean Sea. It is indicated that the hydrographic conditions prevailing in the eastern Cretan Sea affected the structure of the microplanktic community in the surface layer. It is proposed that the assemblage of the identified radiolarian families belonging to Collodaria, Spumellaria (S) and Nassellaria (N), with a high S/N ratio, could be used as biological proxy of weak upwelling systems in the warm stratified waters of the oligotrophic eastern Mediterranean

Heterogeneous and opportunistic wireless networks

Recent years have witnessed the evolution of a large plethora of wireless technologies with different characteristics, as a response of the operators' and users' needs in terms of an efficient and ubiquitous delivery of advanced multimedia services. The wireless segment of network infrastructure has penetrated in our lives, and wireless connectivity has now reached a state where it is considered to be an indispensable service as electricity or water supply. Wireless data networks grow increasingly complex as a multiplicity of wireless information terminals with sophisticated capabilities get embedded in the infrastructure. © 2012 Springer Milan. All Right Reserved

Dynamics of Water and Biofilm Bacterial Community Composition in a Mediterranean Recirculation Aquaculture System

Recirculation technology has been emerging in the marine aquaculture industry. The microbiome developed in recirculation aquaculture systems (RASs) is an important factor for the optimal operation of these systems and fish welfare. In this study, the microbial community dynamics in the water column and the biofilms of a marine RAS with Mediterranean species of gilthead sea bream and sea bass were investigated, while physicochemical conditions were also monitored. Microbiological, culture, and non-culture analyses based on PCR-Denaturing Gradient Gel Electrophoresis (PCR-DGGE) fingerprints were performed on the water column and biofilm developed on stainless-steel surfaces. According to the obtained results, feed administration seemed to cause changes in pH and TAN, as well as drive changes in the bacterial abundance in the water column. Tested surfaces were colonized within 24 h and sessile cells were stabilized in terms of density within 6 days. DGGE fingerprints indicated the stability of the microbial community in water and a dynamic succession in the community of the biofilms. The fish pathogen Tenacibaculum discolor was found to colonize the biofilm and the water column. The main findings confirmed that RAS technology can be used as a control strategy for the stability of the water microbial community, that there is a dynamic succession of the dominant species in the biofilm communities, and that pathogenic bacteria can be dominant in the latter

Measuring objective and subjective well-being: dimensions and data sources

Well-being is an important value for people’s lives, and it could be considered as an index of societal progress. Researchers have suggested two main approaches for the overall measurement of well-being, the objective and the subjective well-being. Both approaches, as well as their relevant dimensions, have been traditionally captured with surveys. During the last decades, new data sources have been suggested as an alternative or complement to traditional data. This paper aims to present the theoretical background of well-being, by distinguishing between objective and subjective approaches, their relevant dimensions, the new data sources used for their measurement and relevant studies. We also intend to shed light on still barely unexplored dimensions and data sources that could potentially contribute as a key for public policing and social development

Predicting seasonal influenza using supermarket retail records

Increased availability of epidemiological data, novel digital data streams, and the rise of powerful machine learning approaches have generated a surge of research activity on realtime epidemic forecast systems. In this paper, we propose the use of a novel data source, namely retail market data to improve seasonal influenza forecasting. Specifically, we consider supermarket retail data as a proxy signal for influenza, through the identification of sentinel baskets, i.e., products bought together by a population of selected customers. We develop a nowcasting and forecasting framework that provides estimates for influenza incidence in Italy up to 4 weeks ahead. We make use of the Support Vector Regression (SVR) model to produce the predictions of seasonal flu incidence. Our predictions outperform both a baseline autoregressive model and a second baseline based on product purchases. The results show quantitatively the value of incorporating retail market data in forecasting models, acting as a proxy that can be used for the real-time analysis of epidemics