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

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

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

Analysis of the precipitation characteristics on the Tibetan Plateau using Remote Sensing, Ground-Based Instruments and Cloud models

In this Thesis work, carried out in the frame of CEOP-AEGIS, an EU FP7 funded project, the problem of the precipitation monitoring over the Tibetan Plateau has been addressed. Despite the Plateau key role in water cycle of South East Asia (and in the life of 1.5 billions of people), there is a critical lack of knowledge, because the current estimates of relevant geophysical parameters are based on sparse and scarce observations than can not provide the required accuracy for quantitative studies and reliable monitoring, especially on a climate change perspective. This is particularly true for precipitation, the geophysical parameter with highest spatial and temporal variability. The constantly increasing availability of Earth system observation from spaceborne sensors makes the remote sensing an effective option for precipitation monitoring and the main focus of the present work is the implementation and applications for three years of data (2008, 2009 and 2010) of an array of satellite precipitation techniques, based on different methodological approaches and data sources. First, a sensitivity study on the capability of the most used satellite sensors to detect precipitation at the ground, assessed with respect to raingauges data for selected case studies, has been carried out. Then, two physically based techniques have been implemented based on satelliteborne active (for snow-rate) and passive (for rain-rate) microwave sensor data and the output used for calibrate geostationary IR-based techniques. Finally, two well established global multisensor precipitation products have been considered for reference and intercomparison. All the techniques have been implemented for the 3 years and the results compared at different spatial and temporal scales. The analysis of daily rain amount has shown that in general global algorithms are able to estimate rain amount larger than the ones estimated by other techniques during the monsoon season. In cold months global techniques underestimate precipitation amount and areas, resulting in a dry bias with respect to IR calibrated techniques. Case studies compared with ground radar precipitation data on convective episodes shown that global products tend to underestimate precipitation areas, while IR calibrated techniques provides reliable rainrate patterns, as compared with radar data. Unfortunately, the number of radar case studies was not large enough to allow significant validation studies, and also non data were available for cold months. Annual precipitation cumulated maps show marked differences among the techniques: IR calibrated techniques generally overestimate precipitation amount by a factor of 2 with respect of global products. Reasons for discrepancies are investigated and discussed, pointing out the uncertainties that will probably be solved only with the exploitation of new satellite missions

Developing a universal single-use skid for continuous purification operations

Please click Additional Files below to see the full abstract

Implementation of an end-to-end continuous bioprocessing platform using novel technologies

One significant opportunity for evolutionary change in the biopharmaceutical industry is the widespread adoption of integrated continuous bioprocessing for biologics manufacturing. Key to its success is the availability of novel upstream and downstream technologies that will not only reduce facility footprint, capital expenses and product cost of goods (CoGs), but also will increase process productivity, flexibility and further facilitate the utilization of single-use and/or disposable technologies. In this context, the suite of cutting-edge technologies we have evaluated to enable cost effective and reliable implementation of continuous bioprocessing of biological drugs, included the Cadence™ Acoustic Separator exploiting acoustic wave separation technology (AWS), Cadence Inline Concentrators within the single-pass TFF (SPTFF) platform, the Cadence BioSMB PD multicolumn continuous chromatography platform using a KANEKA KanCap A™ based platform and novel continuous diafiltration strategies, to address the innovation gap to provide a simplified solution for the continuous final formulation step. By utilizing a 20L CHO fed-batch cell culture bioreactor with cell density range of 25x106 – 30x106 cells/mL and 65 to 90% cell viability, multiple in-house feasibility runs were conducted through a novel integrated continuous bioprocessing train of unit operations. For instance, while achieving ≥90% continuous clarification yield for the processing of a batch with 1.25 g/L titer, 25x106 cells/mL & ~70% viability this new process platform was able to deliver ≥ 2 g/h mAb for the continuous purification train utilizing a stable 4-fold continuous concentration step for the integration of continuous clarification and continuous capture trains. We further intensified the process and by running over a 24h period we were able to purify in excess of 100g mAb over this period giving a productivity of this integrated system of ~124g mAb per day. This was carried out in a dedicated Continuous BioProcessing facility within a footprint of just 36m2. With the coupling of the novel continuous clarification, continuous capture, continuous virus inactivation, continuous polishing, continuous viral clearance and continuous final formulation steps, in this platform, using current PD-scale bioreactors, we have demonstrated end-to-end continuous biomanufacture that will generate 1-5 g/h mAb. This presentation will provide a risk-based and data-driven overview of an integrated continuous bioprocessing platform and highlight the requirements, challenges and opportunities for product development, process monitoring, validati