Heavy metal toxicity as a kill mechanism in impact caused mass extinctions

Heavy metals that are known to be toxic exist in carbonaceous chrondrites at abundances considerably in excess to that of the terrestrial crust. An impactor of relatively undifferentiated cosmic matter would inject into the terrestrial environment large quantities of toxic elements. The abundances of toxic metals found in the Allende CV carbonaceous chondrite and the ratio of meteoritic abundance to crustal abundance are: Cr, 3630 PPM, 30X; Co, 662 PPM, 23X; ni, 13300 PPm, 134X; se, 8.2 PPM, 164X; Os, 0.828 PPM, 166X. The resulting areal density for global dispersal of impactor derived heavy metals and their dilution with terrestrial ejecta are important factors in the determination of the significance of impactor heavy metal toxicity as a kill mechanism in impact caused mass extinctions. A 10 km-diameter asteroid having a density of 3 gram per cu cm would yield a global areal density of impact dispersed chondritic material of 3 kg per square meter. The present areal density of living matter on the terrestrial land surface is 1 kg per square meter. Dilution of impactor material with terrestrial ejecta is determined by energetics, with the mass of ejecta estimated to be in the range of 10 to 100 times that of the mass of the impactor. Because a pelagic impact would be the most likely case, the result would be a heavy metal rainout

Nitrification-denitrification in WSP: a mechanism for permanent nitrogen removal in maturation ponds

A pilot-scale primary maturation pond was spiked with 15N-labelled ammonia (15NH4Cl) and 15N labelled nitrite (Na15NO2), in order to improve current understanding of the dynamics of inorganic nitrogen transformations and removal in WSP systems. Stable isotope analysis of δ15N showed that nitrification could be considered as an intermediate step in WSP, which is masked by simultaneous denitrification, under conditions of low algal activity. Molecular microbiology analysis showed that denitrification can be considered a feasible mechanism for permanent nitrogen removal in WSP, which may be supported either by ammonia-oxidising bacteria (AOB) or by methanotrophs, in addition to nitrite-oxidising bacteria (NOB). However, the relative supremacy of the denitrification process over other nitrogen removal mechanisms (e.g., biological uptake) depends upon phytoplanktonic activity

The history, evolution, and future of big data & analytics:A bibliometric analysis of its relationship to performance in organizations

Big data and analytics (BDA) are gaining momentum, particularly in the practitioner world. Research linking BDA to improved organizational performance seems scarce and widely dispersed though, with the majority focused on specific domains and/or macro-level relationships. In order to synthesize past research and advance knowledge of the potential organizational value of BDA, the authors obtained a data set of 327 primary studies and 1252 secondary cited papers. This paper reviews this body of research, using three bibliometric methods. First, it elucidates its intellectual foundations via co-citation analysis. Second, it visualizes the historical evolution of BDA and performance research and its substreams through algorithmic historiography. Third, it provides insights into the field's potential evolution via bibliographic coupling. The results reveal that the academic attention for the BDA-performance link has been increasing rapidly. The study uncovered ten research clusters that form the field's foundation. While research seems to have evolved following two main, isolated streams, the past decade has witnessed more cross-disciplinary collaborations. Moreover, the study identified several research topics undergoing focused development, including financial and customer risk management, text mining and evolutionary algorithms. The review concludes with a discussion of the implications for different functional management domains and the gaps for both research and practice.</p

Big data-savvy teams’ skills, big data-driven actions and business performance

Prior studies on big data analytics have emphasized the importance of specific big data skills and capabilities for organizational success; however, they have largely neglected to investigate the use of cross-functional teams’ skills and its links to the role played by relevant data-driven actions and business performance. Drawing on the resource-based view (RBV) of the firm and on the data collected from big data experts working in global agrifood networks, we examine the links between the use of big data-savvy (BDS) teams’ skills, big data-driven (BDD) actions and business performance. BDS teams depend on multidisciplinary skills (e.g., computing, mathematics, statistics, machine learning, and business domain knowledge) that help them to turn their traditional business operations into modern data-driven insights (e.g., knowing real time price changes and customer preferences), leading to BDD actions that enhance business performance. Our results, raised from structural equation modelling, indicate that BDS teams' skills that produce valuable insights are the key determinants for BDD actions, which ultimately contribute to business performance. We further demonstrate that those organisations that emphasise BDD actions perform better compared to those that do not focus on such applications and relevant insights

Investigating knowledge management factors affecting Chinese ICT firms performance: An integrated KM framework

This is an Author's Accepted Manuscript of an article published in the Journal of Information Systems Management, 28(1), 19 - 29, 2011, copyright Taylor & Francis, available online at: http://www.tandfonline.com/10.1080/10580530.2011.536107.This article sets out to investigate the critical factors of Knowledge Management (KM) which are considered to have an impact on the performance of Chinese information and communication technology (ICT) firms. This study confirms that the cultural environment of an enterprise is central to its success in the context of China. It shows that a collaborated, trusted, and learning environment within ICT firms will have a positive impact on their KM performance

The Early Days of Research on Carbonic Anhydrase

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73862/1/j.1749-6632.1984.tb12310.x.pd

The development of absorptive capacity-based innovation in a construction SME

Traditionally, construction has been a transaction-oriented industry. However, it is changing from the design-bid-build process into a business based on innovation capability and performance management, in which contracts are awarded on the basis of factors such as knowledge, intellectual capital and skills. This change presents a challenge to construction-sector SMEs with scarce resources, which must find ways to innovate based on those attributes to ensure their future competitiveness. This paper explores how dynamic capability, using an absorptive capacity framework in response to these challenges, has been developed in a construction-based SME. The paper also contributes to the literature on absorptive capacity and innovation by showing how the construct can be operationalized within an organization. The company studied formed a Knowledge Transfer Partnership using action research over a two-year period with a local university. The aim was to increase its absorptive capacity and hence its ability to meet the changing market challenges. The findings show that absorptive capacity can be operationalized into a change management approach for improving capability-based competitiveness. Moreover, it is important for absorptive capacity constructs and language to be contextualized within a given organizational setting (as in the case of the construction-based SME in the present study)

Unsupervised, Efficient and Semantic Expertise Retrieval

We introduce an unsupervised discriminative model for the task of retrieving experts in online document collections. We exclusively employ textual evidence and avoid explicit feature engineering by learning distributed word representations in an unsupervised way. We compare our model to state-of-the-art unsupervised statistical vector space and probabilistic generative approaches. Our proposed log-linear model achieves the retrieval performance levels of state-of-the-art document-centric methods with the low inference cost of so-called profile-centric approaches. It yields a statistically significant improved ranking over vector space and generative models in most cases, matching the performance of supervised methods on various benchmarks. That is, by using solely text we can do as well as methods that work with external evidence and/or relevance feedback. A contrastive analysis of rankings produced by discriminative and generative approaches shows that they have complementary strengths due to the ability of the unsupervised discriminative model to perform semantic matching.Comment: WWW2016, Proceedings of the 25th International Conference on World Wide Web. 201

Low Background Micromegas in CAST

Solar axions could be converted into x-rays inside the strong magnetic field of an axion helioscope, triggering the detection of this elusive particle. Low background x-ray detectors are an essential component for the sensitivity of these searches. We report on the latest developments of the Micromegas detectors for the CERN Axion Solar Telescope (CAST), including technological pathfinder activities for the future International Axion Observatory (IAXO). The use of low background techniques and the application of discrimination algorithms based on the high granularity of the readout have led to background levels below 10$^{-6}$ counts/keV/cm$^2$/s, more than a factor 100 lower than the first generation of Micromegas detectors. The best levels achieved at the Canfranc Underground Laboratory (LSC) are as low as 10$^{-7}$ counts/keV/cm$^2$/s, showing good prospects for the application of this technology in IAXO. The current background model, based on underground and surface measurements, is presented, as well as the strategies to further reduce the background level. Finally, we will describe the R&D paths to achieve sub-keV energy thresholds, which could broaden the physics case of axion helioscopes.Comment: 6 pages, 3 figures, Large TPC Conference 2014, Pari

Lowering the background level and the energy threshold of Micromegas x-ray detectors for axion searches

Axion helioscopes search for solar axions by their conversion in x-rays in the presence of high magnetic fields. The use of low background x-ray detectors is an essential component contributing to the sensitivity of these searches. In this work, we review the recent advances on Micromegas detectors used in the CERN Axion Solar Telescope (CAST) and proposed for the future International Axion Observatory (IAXO). The actual setup in CAST has achieved background levels below 10$^{-6}$ keV$^{-1}$ cm$^{-2}$ s$^{-1}$, a factor 100 lower than the first generation of Micromegas detectors. This reduction is based on active and passive shielding techniques, the selection of radiopure materials, offline discrimination techniques and the high granularity of the readout. We describe in detail the background model of the detector, based on its operation at CAST site and at the Canfranc Underground Laboratory (LSC), as well as on Geant4 simulations. The best levels currently achieved at LSC are low than 10$^{-7}$ keV$^{-1}$ cm$^{-2}$ s$^{-1}$ and show good prospects for the application of this technology in IAXO. Finally, we present some ideas and results for reducing the energy threshold of these detectors below 1 keV, using high-transparent windows, autotrigger electronics and studying the cluster shape at different energies. As a high flux of axion-like-particles is expected in this energy range, a sub-keV threshold detector could enlarge the physics case of axion helioscopes.Comment: Proceedings of 3rd International Conference on Technology and Instrumentation in Particle Physics (TIPP 2014