2.5K-Graphs: from Sampling to Generation

Understanding network structure and having access to realistic graphs plays a central role in computer and social networks research. In this paper, we propose a complete, and practical methodology for generating graphs that resemble a real graph of interest. The metrics of the original topology we target to match are the joint degree distribution (JDD) and the degree-dependent average clustering coefficient ($\bar{c}(k)$). We start by developing efficient estimators for these two metrics based on a node sample collected via either independence sampling or random walks. Then, we process the output of the estimators to ensure that the target properties are realizable. Finally, we propose an efficient algorithm for generating topologies that have the exact target JDD and a $\bar{c}(k)$ close to the target. Extensive simulations using real-life graphs show that the graphs generated by our methodology are similar to the original graph with respect to, not only the two target metrics, but also a wide range of other topological metrics; furthermore, our generator is order of magnitudes faster than state-of-the-art techniques

A Network Coding Approach to Loss Tomography

Network tomography aims at inferring internal network characteristics based on measurements at the edge of the network. In loss tomography, in particular, the characteristic of interest is the loss rate of individual links and multicast and/or unicast end-to-end probes are typically used. Independently, recent advances in network coding have shown that there are advantages from allowing intermediate nodes to process and combine, in addition to just forward, packets. In this paper, we study the problem of loss tomography in networks with network coding capabilities. We design a framework for estimating link loss rates, which leverages network coding capabilities, and we show that it improves several aspects of tomography including the identifiability of links, the trade-off between estimation accuracy and bandwidth efficiency, and the complexity of probe path selection. We discuss the cases of inferring link loss rates in a tree topology and in a general topology. In the latter case, the benefits of our approach are even more pronounced compared to standard techniques, but we also face novel challenges, such as dealing with cycles and multiple paths between sources and receivers. Overall, this work makes the connection between active network tomography and network coding

Network Evolution Based on Centrality

We study the evolution of networks when the creation and decay of links are based on the position of nodes in the network measured by their centrality. We show that the same network dynamics arises under various centrality measures, and solve analytically the network evolution. During the complete evolution, the network is characterized by nestedness: the neighbourhood of a node is contained in the neighbourhood of the nodes with larger degree. We find a discontinuous transition in the network density between hierarchical and homogeneous networks, depending on the rate of link decay. We also show that this evolution mechanism leads to double power-law degree distributions, with interrelated exponents.Comment: 6 pages, 3 figure

Accumulation of Heavy Metals in Vegetables from Agricultural Soils

This study analyzed the heavy metals in vegetables cultivated in private gardens in Bregu i Matit, an important agricultural area in the NW Albania.The plant and soil samples taken from irrigated and non-irrigated fields in this area were analyzed for the concentrations of Cd, Cu, Zn, Pb and Ni using atomic absorption spectroscopy (AAS), after extraction by HNO3 and H2O2.The transfer factors (TF) were used to evaluate the risk of metal transfer from soil to plant and the FAO/WHO safe limits to assess the potential hazards of heavy metals to human health. The ranges of heavy metal concentrations \ub1 standard deviation in vegetable samples were (mg kg-1): Cu 2.98-12.90 (\ub13.08), Ni 4.82-35.79 (\ub17.68), Zn Zn > Cu > Ni > Pb. The TF values indicate that only Cd was accumulated in plants.The contents of Cd in three vegetable samples, Pb in four samples, and Cu in one sample were above the safe limits set by the FAO/WHO for heavy metals in foods and vegetables indicating that consumption of vegetables grown in the studied soils could be dangerous to human health

Digital contracts, the spread of digital currency, advantages, innovation and problems stemming from them

This article aims to provide an overview of the creation, development and evolution that banking and the banking system is going through these days. It addresses the concept of digital contracts, their creation and their approach to various banks and banking systems. Afterwards, the article focuses on digital banking and digital activity of today's banks, the application of digital contracts, their content, and digital and online content. This article considers the establishment and development of various digital currencies, and recently, the launch of a new digital currency from Facebook. Looking at the currency expected to be launched by Facebook, various studies show that such currency is increasingly taking ground in the area of economic transactions, replacing thus successfully the traditional currency

Analyzing the Effects of Mathematical Mindsets and Self-regulation of Middle School Students to Overcome the Challenges of Math Anxiety

In the rapidly changing world, schools must prepare students for jobs and careers that may not exist today. Mathematics is one of the core subject areas that help students prepare to meet the demands of the 21st-century. When students are proficient in mathematics, they have the opportunity to build problem-solving skills. Learning mathematics helps students find solutions to a problem logically and develop analytical thinking skills. However, many students struggle with mathematical content and concepts during math lessons and learning activities. Teachers need to create practical age-appropriate lessons focusing on problem-solving skills to help students who fear math and experience math anxiety. Skilled teachers make a difference, especially when working with students who have different learning styles and abilities. Engaging students in meaningful math learning activities in the early years of schooling is necessary because it helps them create a solid foundation for future success in mathematics and life. This Improvement Science Dissertation in Practice study aimed to analyze the effects of self-regulation and mathematical mindsets development of middle school students to reduce feelings of fear and anxiety when learning mathematics

Fine-Mapping the Results From Genome-Wide Association Studies of Primary Biliary Cholangitis Using Susie and h2-D2

\ua9 2024 The Author(s). Genetic Epidemiology published by Wiley Periodicals LLC. The main goal of fine-mapping is the identification of relevant genetic variants that have a causal effect on some trait of interest, such as the presence of a disease. From a statistical point of view, fine mapping can be seen as a variable selection problem. Fine-mapping methods are often challenging to apply because of the presence of linkage disequilibrium (LD), that is, regions of the genome where the variants interrogated have high correlation. Several methods have been proposed to address this issue. Here we explore the ‘Sum of Single Effects’ (SuSiE) method, applied to real data (summary statistics) from a genome-wide meta-analysis of the autoimmune liver disease primary biliary cholangitis (PBC). Fine-mapping in this data set was previously performed using the FINEMAP program; we compare these previous results with those obtained from SuSiE, which provides an arguably more convenient and principled way of generating ‘credible sets’, that is set of predictors that are correlated with the response variable. This allows us to appropriately acknowledge the uncertainty when selecting the causal effects for the trait. We focus on the results from SuSiE-RSS, which fits the SuSiE model to summary statistics, such as z-scores, along with a correlation matrix. We also compare the SuSiE results to those obtained using a more recently developed method, h2-D2, which uses the same inputs. Overall, we find the results from SuSiE-RSS and, to a lesser extent, h2-D2, to be quite concordant with those previously obtained using FINEMAP. The resulting genes and biological pathways implicated are therefore also similar to those previously obtained, providing valuable confirmation of these previously reported results. Detailed examination of the credible sets identified suggests that, although for the majority of the loci (33 out of 56) the results from SuSiE-RSS seem most plausible, there are some loci (5 out of 56 loci) where the results from h2-D2 seem more compelling. Computer simulations suggest that, overall, SuSiE-RSS generally has slightly higher power, better precision, and better ability to identify the true number of causal variants in a region than h2-D2, although there are some scenarios where the power of h2-D2 is higher. Thus, in real data analysis, the use of complementary approaches such as both SuSiE and h2-D2 is potentially warranted

Web Data Extraction, Applications and Techniques: A Survey

Web Data Extraction is an important problem that has been studied by means of different scientific tools and in a broad range of applications. Many approaches to extracting data from the Web have been designed to solve specific problems and operate in ad-hoc domains. Other approaches, instead, heavily reuse techniques and algorithms developed in the field of Information Extraction. This survey aims at providing a structured and comprehensive overview of the literature in the field of Web Data Extraction. We provided a simple classification framework in which existing Web Data Extraction applications are grouped into two main classes, namely applications at the Enterprise level and at the Social Web level. At the Enterprise level, Web Data Extraction techniques emerge as a key tool to perform data analysis in Business and Competitive Intelligence systems as well as for business process re-engineering. At the Social Web level, Web Data Extraction techniques allow to gather a large amount of structured data continuously generated and disseminated by Web 2.0, Social Media and Online Social Network users and this offers unprecedented opportunities to analyze human behavior at a very large scale. We discuss also the potential of cross-fertilization, i.e., on the possibility of re-using Web Data Extraction techniques originally designed to work in a given domain, in other domains.Comment: Knowledge-based System