‘Maidan has become part of Ukrainian identity’: the dynamics of naming and framing civil resistance in parliamentary discourse

As broad deployed of wireless sensor networks, privacy concerns have emerged as the main obstacle to success. When wireless sensor networks are used in everyday life, the privacy about monitored object' sensitive data becomes an important issue. Consequently, providing efficient data aggregation privacy protection is desirable. However, the existing technique is always energy exhausting, and does not consider different privacy levels of data aggregation. In this paper, DADPP (Data Aggregation Different Privacy-Levels Protection) is proposed to deal with data aggregation privacy protection. DADPP offers different levels of data aggregation privacy based on different node-numbers for pretreating data. According to desired privacy level, all nodes within the same cluster are partitioned into many groups, any group including node- numbers belong to the same privacy level. Data are pretreated only in the same group. Compared with the existing technique, DADPP has lower energy costs while ensuring expected privacy level.

Reserving problem in non-life insurance is an applied statistical problem with several computational aspects. In the literature, the focus has mainly been on aggregate reserving techniques and the chain-ladder method with its extensions has remained as the most widely applied claim reserving method. The classical chain-ladder method is regularly applied to annual data, but the question arises whether the reserve estimates based on more refined data outperform results obtained by annual data. We investigate whether and how much different data aggregation levels can improve the reserving process. To compare the performance of the classical chain-ladder method and its novel continuous extension, we conduct two simulation studies as well as a case study with an insurance data and use both models to estimate the IBNR claim count estimates on different levels of data aggregation. The results demonstrate that the continuous approach of the chain-ladder method provides in general a minor improvement in the IBNR predictions but proves to hold its predictive power on different types of data. This study highlights data aggregation levels in which the estimates of classical chain-ladder model outperforms the estimates obtained by the use of annual data, providing more insights for establishing accurate loss reserves.

Brand personality and human personality: Findings from ratings of familiar Croatian brands

In this paper, we present a suite of visualization techniques for sensor-based transportation system data at different scales to facilitate the exploration of interconnected traffic dynamics at intersections and highways. These techniques are designed for analyzing multivariate traffic data from radar-based highway sensors and camera-based intersection sensors recording turn movements and vehicle speed, in the Chattanooga Metropolitan Area, with the capability of (a) revealing multiscale mobility patterns using different levels of data aggregation (e.g., individual sensor for microscale, multiple sensors along a corridor for mesoscale, and a larger number of sensors across the region for macroscale visualization) at different intervals (e.g., 5-min intervals, time of day, full day, and day-of-the-week), and (b) exploring the spatial variation of multiple traffic-related variables (e.g., volumes, speeds, turn movements, and traffic light colors) provided by the sensors. We close with a case study to demonstrate the effectiveness of our multiscale and multivariate visualization techniques. At microscale, we focused on intersection data from a shopping district around Shallowford Road in East Chattanooga. For mesoscale visualization, we studied the Shallowford Road corridor and an adjacent stretch of I-75. At macroscale, we included highway data from the Chattanooga Metropolitan Area. All visualizations were integrated into a web-based situational awareness tool to promote user access and interaction. At a minimum, each visualization provides the option for selecting dates for real-time (depending on sensor availability) and historical data, and additional information on hovering, though most provide more detailed information, including different views of the selected data, or interactive highlights.

Using a comprehensive international trade data set we investigate empirical regularities (known as Zipf’s Law or the rank-size rule) for the distribution of the interaction between countries as measured by revealed comparative advantage. Using the recently developed estimator by Gabaix and Ibragimov (2006) we find strong evidence in favor of the rank-size rule along the time, country, and sector dimension for three different levels of data aggregation. The estimated power exponents that characterize the distribution of revealed comparative advantage are stable over time but differ between countries and sectors. These differences are related empirically to country and sector characteristics, including population size, GDP, and factor intensities.

Membership in voluntary organizations is associated with individual and social benefits. Due to the negative consequences of the global pandemic on older people, and the governmental challenges posed by population aging, voluntary membership is of great importance to society. To effectively promote volunteering among older people, it is necessary to understand the determinants of voluntary membership. This study analyses the influence of individual values—secular/traditional and survival/self-expression–on voluntary membership among European adults (N = 31,985). Specifically, it examines which values orient two age groups (middle age: 50–64 and old age: 65–79), as well as men and women toward a certain type of association (Social Awareness; Professional and Political; Education and Leisure; Religion). The sample of 31,985 comprises 60% of adults aged 50–64 and 40% aged 65–79; of which 56% are women and 44% men. The empirical estimation considers different levels of data aggregation: individual, national and welfare system, therefore multilevel analysis is used as an analytical strategy. Individual-level variables from the Integrated Values Survey (2005/09, 2010/14, and 2017/20) and national-level variables (Gini Index and Gross Domestic Product Per Capita) from the World Bank and Eurostat are used. The results indicate that traditional and self-expression values promote membership in voluntary organizations in general more than secular and survival values. However, there are differences according to the type of organization. Furthermore, values are found to moderate the effect of age and gender on voluntary membership.

The forensics of fraud: Evidence from the 2018 Brazilian presidential election

Pupillometry - the study of temporal changes in pupil diameter as a function of external light stimuli or cognitive processing - requires the accurate and gaze-angle independent measurement of pupil dilation. Expected response amplitudes often are only a few percent relative to a pre-stimulus baseline, thus demanding for sub-millimeter accuracy. Video-based approaches to pupil-size measurement aim at inferring pupil dilation from eye images alone. Eyeball rotation in relation to the recording camera as well as optical effects due to refraction at corneal interfaces can, however, induce so-called pupil foreshortening errors (PFE), i.e. systematic gaze-angle dependent changes of apparent pupil size that are on a par with typical response amplitudes. While PFE and options for its correction have been discussed for remote eye trackers, for head-mounted eye trackers such an assessment is still lacking. In this work, we therefore gauge the extent of PFE in three measurement techniques, all based on eye images recorded with a single near-eye camera. We present both real world experimental data as well as results obtained on synthetically generated eye images. We discuss PFE effects at three different levels of data aggregation: the sample, subject, and population level. In particular, we show that a recently proposed refraction-aware approach employing a mathematical 3D eye model is successful in providing pupil-size measurements which are gaze-angle independent at the population level.

Using a comprehensive international trade data set we document empirical Power Laws (PLs) for the distribution of the interaction between countries as measured by revealed comparative advantage. Using the recently developed estimator by Gabaix and Ibragimov (2011), we find strong evidence in favour of PLs along the time, country and sector dimension for three different levels of data aggregation. This finding is not predicted by any of the existing trade theories. The estimated PL exponents characterizing the distribution of revealed comparative advantage are stable over time but differ between countries and sectors. These differences are related empirically to country and sector characteristics, including population size, Gross Domestic Product (GDP) and factor intensities.

The effect of data aggregation on temporal stability of cardiovascular reactivity

A comprehensive evacuation planning model and genetic solution algorithm

Quantifying geomorphological heterogeneity to assess species diversity of set-aside arable land

With the expansion of pollution-prevention initiatives in the government sector, development of certification and eco-labeling mechanisms in foreign trade, and the emergence of “green” market drivers for consumer demand, industry is under increasing pressure to evaluate the “life-cycle” waste streams which emanate from their products and manufacturing processes. While much research has been devoted to the study of “system-level” design-for-environment (i.e. design for disassembly, serviceability, modularity), little attention has been given to the influence of planning and design decisions at the unit manufacturing process level, which has a significant impact on waste streams through material, catalyst, parameter and feature selection decisions. One of the most pressing issues in environmentally-conscious manufacturing is the ability to compare the environmental impacts of dissimilar waste streams to formulate the above decisions. This paper presents an overview of the hierarchical levels of comparative waste assessment which links process-level emissions to immediate, site-wide, and eco-system impacts. Significant issues to be addressed are: (1) the aggregation of data collection required for each level of decision-making, (2) the range of environmental effects needed to be analyzed at each level, (3) the uncertainty present at different levels of data aggregation, (4) the influence of site-specific (fate and transport) factors, and (5) the transformation of environmental information into metrics usable in detailed design and planning of products and processes. Case studies in the fabrication of metal parts and printed circuit boards are presented.

Supply chain design under uncertainty using sample average approximation and dual decomposition

Functional trait-based approaches have undergone an extraordinary expansion in phytoplankton ecology. Morpho-functional traits have been shown to vary both within and between populations and species, potentially affecting individual fitness and the network of inter-individual relationships. Here we integrate six fully harmonized phytoplankton morpho-functional trait datasets, characterized by a fine data grain, reporting individual-level data over a large biogeographical area. Datasets refer to transitional water ecosystems, from five biogeographical areas: Northern Atlantic Ocean (Scotland), South-Western Atlantic Ocean (Brazil), South-Western Pacific Ocean (Australia), Indo Pacific Ocean (Maldives) and Mediterranean Sea (Greece and Turkey). The integrated dataset includes 127311 individual phytoplankton records with sampling locations, taxonomic and morphometric information according to Darwin Core standards and semantic annotations. The six FAIR datasets are openly available in the LifeWatch Italy data portal. The datasets have already been used for morpho-functional analyses and hypothesis testing on phytoplankton guilds at different levels of data aggregation and scale, from local to global.