Boosting landfill gas production from lignin-containing wastes via termite hindgut microorganism.

Lignocellulose comprises a significant portion of municipal solid waste (MSW) - 40-70% in developed countries, including paper, wood, and yard waste. Cellulose and hemicellulose are often shielded by lignin, posing a barrier to waste decomposition and landfill gas generation. Unfortunately, lignin is resistant to microbial degradation under low-oxygen conditions that normally occur in MSW landfills. The bacterium strain TAV5, microaerophilic and member of phylum Verrucomicrobia, isolated from the hindgut of the Reticulitermes flavipes termite, the most widely distributed subterranean termite in North America. Its genome contains genes associated with methylotrophic competency which code for enzymes that structurally modify lignin. The overall goal of this research was to use TAV5 to modify lignin and boost methane production from MSW. Batch-scale reactors (125 mL) were filled with paper, yard, or wood waste, and four ratios of mixed of waste. Reactors were seeded with different ratios of TAV5 to anaerobic digester (AD) microorganisms (representing landfill anaerobic microorganisms). Based on batch tests, optimal ratios of TAV5 to AD microorganisms were used to seed wastes (mixed, yard, and wood) in 6-gallon reactors. Addition of TAV5 increased methane production from mixed waste, yard waste, and wood, by 49%, 34%, and 297%, respectively. TAV5 decreased acid soluble lignin by 7-39%, depending on waste type. TAV5 grown under aerobic conditions and room temperature (not requiring a heated anaerobic chamber) was found to remain viable and increase methane production under low-level oxygen conditions (1-2%). This finding will potentially lessen costs for growing large volumes of it for seeding landfills

Rice research, technological progress, and impacts on the poor: the Bangladesh case (summary report)

"This case study builds on an ongoing large-scale quantitative research project undertaken by BIDS/IRRI since 1987 originally in 64 unions from 57 districts of the country. It adds a qualitative research component to examine the impact of modern rice varieties (MVs) on livelihoods in a structured sample of eight of these villages across a range of favorable and unfavorable contexts..... The quantitative research shows that for households with access to land there have been direct adoption impacts in the form of increased yields and higher profits. However, since rice now only represents around 20 percent of most households' overall income, nonagricultural income is found to have gained dramatically in importance for rural households. While the profitability has declined over time, rice contributes to improved food security and provides a “springboard” for both rich and poor farm households moving into nonfarm income generation and employment... The qualitative research component generally confirmed these general findings, highlighting other factors such as the improved status associated with fixed-rent tenancy and “contract” labor arrangements. The qualitative research also shows negative adoption impacts such as shrinking common property resources (wild fish, vegetables, etc., and declining soil fertility, both of which may increase the long-term vulnerability of the poor. It also throws light on the processes of technology dissemination.... It was found that the linking of qualitative and quantitative research methodologies was useful in (a) generating complementary data of different kinds on similar issues and (b) generating new data missed within a purely quantitative approach. The sustainable livelihoods framework was a useful, flexible tool for structuring the qualitative data collection and analysis. However, the research study as a whole was limited by the fact that the qualitative component was “bolted onto” a quantitative study already underway. Therefore the framework, and the various data collection methodologies, were not systematically integrated across both components of the study. In conclusion, future agricultural research on rice may need to further address the question of MV adoption potential on risk-prone lands, the relevance of existing technology dissemination systems, the relationship between MV adoption and crop diversification, and the challenges of more sustainable crop management techniques." Authors' AbstractRice Bangladesh,

Magnetic Transition in the Kondo Lattice System CeRhSn2

Our resistivity, magnetoresistance, magnetization and specific heat data provide unambiguous evidence that CeRhSn2 is a Kondo lattice system which undergoes magnetic transition below 4 K.Comment: 3 pages text and 5 figure

Dyad and Triad Census Analysis of Crisis Communication Network

have been found very useful in analyzing structural properties of social networks. This study aims to explore crisis communication network by following dyad and triad census analysis approach to investigate the association of mi- cro-level communication patterns with organizational crisis. This study further tests hypothesis related to the process of data generation and tendency of the structural pattern of transitivity using dyad and triad census output. The changing communication network at Enron Corporation during the period of its crisis is analyzed in this study. Significant dif- ferences in the presence of different isomorphism classes or micro-level patterns of both dyad and triad census are no- ticed in crisis and non-crisis period network of Enron email corpus. It is also noticed that crisis communication network shows more transitivity comppublished_or_final_versio

Effect of Ni-doping on magnetism and superconductivity in Eu0.5K0.5Fe2As2

The effect of Ni-doping on the magnetism and superconductivity in Eu0.5K0.5Fe2As2 has been studied through a systematic investigation of magnetic and superconducting properties of Eu0.5K0.5(Fe1-xNix)2As2 (x = 0, 0.03, 0.05, 0.08 and 0.12) compounds by means of dc and ac magnetic susceptibilities, electrical resistivity and specific heat measurements. Eu0.5K0.5Fe2As2 is known to exhibit superconductivity with superconducting transition temperature Tc as high as 33 K. The Ni-doping leads to a rapid decrease in Tc; Tc is reduced to 23 K with 3% Ni-doping, and 8% Ni-doping suppresses the superconductivity to below 1.8 K. In 3% Ni-doped sample Eu0.5K0.5(Fe0.97Ni0.03)2As2 superconductivity coexists with short range ordering of Eu2+ magnetic moments at Tm ~ 6 K. The suppression of superconductivity with Ni-doping is accompanied with the emergence of a long range antiferromagnetic ordering with TN = 8.5 K and 7 K for Eu0.5K0.5(Fe0.92Ni0.08)2As2 and Eu0.5K0.5(Fe0.88Ni0.12)2As2, respectively. The temperature and field dependent magnetic measurements for x = 0.08 and 0.12 samples reflect the possibility of a helical magnetic ordering of Eu2 moments. We suspect that the helimagnetism of Eu spins could be responsible for the destruction of superconductivity as has been observed in Co-doped EuFe2As2. The most striking feature seen in the resistivity data for x = 0.08 is the reappearance of the anomaly presumably due to spin density wave transition at around 60 K. This could be attributed to the compensation of holes (K-doping at Eu-site) by the electrons (Ni-doping at Fe site). The anomaly associated with spin density wave further shifts to 200 K for x = 0.12 for which the electron doping has almost compensated the holes in the system.Comment: 9 pages, 10 figure

Collective Dynamics of Hierarchical Networks

In an increasingly complex, mobile and interconnected world, we face growing threats of disasters, whether by chance or deliberately. Disruption of coordinated response and recovery efforts due to organizational, technical, procedural, random or deliberate attack could result in the risk of massive loss of life. This requires urgent action to explore the development of optimal information-sharing environments for promoting collective disaster response and preparedness using multijurisdictional hierarchical networks. Innovative approaches to information flow modeling and analysis for dealing with challenges of coordinating across multi layered agency structures as well as development of early warnings through social systems using social media analytics may be pivotal to timely 2 responses to dealing with large scale disasters where response strategies need to be viewed as a shared responsibility. How do facilitate the development of collective disaster response in a multijurisdictional setting? How do we develop and test the level and effectiveness of shared multijurisdictional hierarchical networks for improved preparedness and response? What is the role of multi layered training and exercises in building the shared learning space for collective disaster preparedness and response? The aim of this is therefore to determine factors that may be responsible for affecting disaster response. It is shown here that changes to the interconnectedness of nodes in the network may have implications on the potential to preparedness and response. In this study, theory behind social network analysis is applied to a large-scale inter-organisational Disaster Response Network (DRN) for exploring correlation between network interconnectedness and response. We discover that the leadership and involvement displayed by organisations in multijurisdictional emergency response is not equal, and hypothesise the existence of a loose tiered structure that guides how interconnected an organisation should be. A model is presented as a theoretical means to confront the issues of disaster response. To test our hypotheses, we investigate survey data from state law enforcement, state emergency services and local law enforcement by performing agency-based (macro) and cross-agency (micro) analysis to identify attributes of each network and response. Results suggest that there is a positive correlation between network connectedness and potential to effective response as well as the concept of tiers within DRN may exist which can be characterized by the sub-network that an organisation associates with.preprin

Network Effects on Scientific Collaborations

Background: The analysis of co-authorship network aims at exploring the impact of network structure on the outcome of scientific collaborations and research publications. However, little is known about what network properties are associated with authors who have increased number of joint publications and are being cited highly. Methodology/Principal Findings: Measures of social network analysis, for example network centrality and tie strength, have been utilized extensively in current co-authorship literature to explore different behavioural patterns of co-authorship networks. Using three SNA measures (i.e., degree centrality, closeness centrality and betweenness centrality), we explore scientific collaboration networks to understand factors influencing performance (i.e., citation count) and formation (tie strength between authors) of such networks. A citation count is the number of times an article is cited by other articles. We use co-authorship dataset of the research field of 'steel structure' for the year 2005 to 2009. To measure the strength of scientific collaboration between two authors, we consider the number of articles co-authored by them. In this study, we examine how citation count of a scientific publication is influenced by different centrality measures of its co-author(s) in a co-authorship network. We further analyze the impact of the network positions of authors on the strength of their scientific collaborations. We use both correlation and regression methods for data analysis leading to statistical validation. We identify that citation count of a research article is positively correlated with the degree centrality and betweenness centrality values of its co-author(s). Also, we reveal that degree centrality and betweenness centrality values of authors in a co-authorship network are positively correlated with the strength of their scientific collaborations. Conclusions/Significance: Authors' network positions in co-authorship networks influence the performance (i.e., citation count) and formation (i.e., tie strength) of scientific collaborations. © 2013 Uddin et al.published_or_final_versio

Percolation Centrality: Quantifying Graph-Theoretic Impact of Nodes during Percolation in Networks

A number of centrality measures are available to determine the relative importance of a node in a complex network, and betweenness is prominent among them. However, the existing centrality measures are not adequate in network percolation scenarios (such as during infection transmission in a social network of individuals, spreading of computer viruses on computer networks, or transmission of disease over a network of towns) because they do not account for the changing percolation states of individual nodes. We propose a new measure, percolation centrality, that quantifies relative impact of nodes based on their topological connectivity, as well as their percolation states. The measure can be extended to include random walk based definitions, and its computational complexity is shown to be of the same order as that of betweenness centrality. We demonstrate the usage of percolation centrality by applying it to a canonical network as well as simulated and real world scale-free and random networks. © 2013 Piraveenan et al.published_or_final_versio