Topology and seasonal evolution of the network of extreme precipitation over the Indian subcontinent and Sri Lanka

Peer reviewedPublisher PD

On the influence of spatial sampling on climate networks

Peer reviewedPublisher PD

Characterizing the evolution of climate networks

Complex network theory has been successfully applied to understand the structural and functional topology of many dynamical systems from nature, society and technology. Many properties of these systems change over time, and, consequently, networks reconstructed from them will, too. However, although static and temporally changing networks have been studied extensively, methods to quantify their robustness as they evolve in time are lacking. In this paper we develop a theory to investigate how networks are changing within time based on the quantitative analysis of dissimilarities in the network structure. Our main result is the common component evolution function (CCEF) which characterizes network development over time. To test our approach we apply it to several model systems, ErdA's-Rényi networks, analytically derived flow-based networks, and transient simulations from the START model for which we control the change of single parameters over time. Then we construct annual climate networks from NCEP/NCAR reanalysis data for the Asian monsoon domain for the time period of 1970-2011 CE and use the CCEF to characterize the temporal evolution in this region. While this real-world CCEF displays a high degree of network persistence over large time lags, there are distinct time periods when common links break down. This phasing of these events coincides with years of strong El Niño/Southern Oscillation phenomena, confirming previous studies. The proposed method can be applied for any type of evolving network where the link but not the node set is changing, and may be particularly useful to characterize nonstationary evolving systems using complex networks

Characterizing the evolution of climate networks

Peer reviewedPublisher PD

Harnessing collective intelligence for the future of learning – a co-constructed research and development agenda

Learning, defined as the process of constructing meaning and developing competencies to act on it, is instrumental in helping individuals, communities, and organizations tackle challenges. When these challenges increase in complexity and require domain knowledge from diverse areas of expertise, it becomes difficult for single individuals to address them. In this context, collective intelligence, a capacity of groups of people to act together and solve problems using their collective knowledge, becomes of great importance. Technologies are instrumental both to support and understand learning and collective intelligence, hence the need for innovations in the area of technologies that can support user needs to learn and tackle collective challenges. Use-inspired research is a fitting paradigm that spans applied solutions and scientific explanations of the processes of learning and collective intelligence, and that can improve the technologies that may support them. Although some conceptual and theoretical work explaining and linking learning with collective intelligence is emerging, technological infrastructures as well as methodologies that employ and evidence that support them are nascent. We convened a group of experts to create a middleground and engage with the priorities for use-inspired research. Here we detail directions and methods they put forward as most promising for advancing a scientific agenda around learning and collective intelligence

Effect of inoculum size on the rates of whole ore colonisation of mesophilic, moderate thermophilic;thermophilic acidophiles

Bioheap leaching of low grade copper sulphides has been applied successfully at the commercial scale for the extraction of copper from secondary sulphide minerals. It is important to optimise the inoculation of heaps in order to minimise the residence time required for the heap and to maximise extraction. Thermophilic bioleaching of the primary sulphide chalcopyrite poses an additional challenge of rising temperatures inside the heap demanding microbial succession. After heap start up, rising heap core temperatures make conditions less favourable for mesophilic microbial species, and the moderately thermophilic community succeeds them in the consortium. In turn, thermophilic microorganisms succeed the moderately thermophilic microbes as the higher temperatures are reached. A detailed understanding of the microbial colonisation of whole ore is necessary to optimise microbial succession during thermophilic bioleaching, as is that of microbial growth kinetics on whole ore. Most published research is focused on microbial growth rates of bioleaching organisms in liquid cultures; little work is reported on microbial colonisation of whole ore and subsequent microbial activity. To extend the information available on the microbial diversity and succession in a bioleaching habitat, a study of bioleaching microbes colonising the ore body is required. The aim of this work was to explore aspects of colonisation of low grade chalcopyrite ore at 23 °C, 50 °C and 65 °C by acidophilic micro-organisms. Laboratory column packed bed reactors were designed to simulate heap leach environments and to provide a systematic way of studying microbial dynamics on whole ore. The effect of inoculum size and inoculation strategies on microbial activity established and the subsequent leaching performance were investigated under conditions that support mesophilic, moderately thermophilic and thermophilic microorganisms. A relationship was shown between the inoculum size and the culture time required to achieve Eh values greater than 700 mV, especially at 23 °C and 65 °C. However, the culture time required to establish an active iron- (and sulphur-) oxidising culture was also influenced by ore type, irrigation rate and inoculum adaptation. The effect on effluent Eh, pH and dissolved iron levels is also discussed

An experimental study of the long-term bioleaching of large sphalerite ore particles in a circulating fluid fixed-bed reactor

Although the bioleaching of sulphide ores is well studied, the relatively coarse particle size typical of heap and dump leach operations, presents unique challenges. Leaching from such large particles, especially over long-term process, is poorly understood. In this study, three large particle size classes (+ 23/- 25, + 14/- 16, + 5.25/- 6.75 mm) were prepared from a sphalerite ore from the Northern Cape, South Africa, by two different methods of comminution (HPGR and cone crusher) and packed into leach reactors, which were operated continuously and well mixed through internal circulation of leach solution. Each reactor was inoculated by a mixed culture of mesophilic acidophilic chemolithotrophs (1.86 � 10 10 cell/kg ore) in which L. ferriphilum was predominant. This culture had been adapted to the sphalerite ore sample previously. Media containing 1 g/L ferrous iron were used as continuous feed to the reactors. The pH, redox potential, Fe + 3 and Fe + 2 concentration as well as total Fe, Zn, Mg, Al and planktonic cell concentrations in the effluent solution were measured regularly. The reactors were stopped from time to time to investigate the progress of leaching and also the colonisation of the surface of the ore particles by sessile cells was investigated. Planktonic and sessile microbial populations were monitored by quantitative real time polymerase chain reaction (qRT PCR). A comparison of the effect of the different comminution devices on metal extraction indicated that HPGR crushed ore leached more rapidly in all particle size classes and showed 10 to 15 additional zinc leach extraction. On average, larger particles showed a greater difference in the cell population densities (HPGR crushed ore has more cell population) than the finer particles, and L. ferriphilum continued to dominate the microbial population over the course of leaching. © 2012 Elsevier B.V

Use of modified Harvard step test for the evaluation of exercise tolerance in patients with a functional single ventricle after total cavopulmonary connection

According to the literature, nearly one-third of patients with congenital heart diseases have signs of heart failure. Objective: to assess the possibility and results of using a modified Harvard step test (MHST) for the evaluation of exercise tolerance in children with a functional single ventricle. The investigation covered 110 healthy children aged 6 to 16 years and 29 patients aged 3 to 16 years with a functional single ventricle after total cavopulmonary connection with an extracardiac conduit a year after surgery and fenestration closure. MHST using a complete protocol was carried out in 44,8% of the patients. In the other examinees, the reason for stopping the test was premature muscle weakness and dyspnea. This could establish Functional Class (FC) II heart failure in 55,2% of the sick children. In the examinees, the MHST index (MHSTI) characterizing exercise tolerance ranged from 22,4 to 111. The median MHSTI scores significantly differed between the groups of patients with FC I and II heart failure (p=0,021). Exercise tolerance was lower in 17,2% of the patients with a functional single ventricle; in the others it was average and above average (41,5 and 41,3%, respectively), which was suggestive of good hemodynamic adaptation in patients after surgery. The findings prove the safety and efficiency of using the above test in the evaluation of exercise tolerance in children 3 years of age and older

Interdisciplinary research in artificial intelligence: challenges and opportunities

The use of artificial intelligence (AI) in a variety of research fields is speeding up multiple digital revolutions, from shifting paradigms in healthcare, precision medicine and wearable sensing, to public services and education offered to the masses around the world, to future cities made optimally efficient by autonomous driving. When a revolution happens, the consequences are not obvious straight away, and to date, there is no uniformly adapted framework to guide AI research to ensure a sustainable societal transition. To answer this need, here we analyze three key challenges to interdisciplinary AI research, and deliver three broad conclusions: 1) future development of AI should not only impact other scientific domains but should also take inspiration and benefit from other fields of science, 2) AI research must be accompanied by decision explainability, dataset bias transparency as well as development of evaluation methodologies and creation of regulatory agencies to ensure responsibility, and 3) AI education should receive more attention, efforts and innovation from the educational and scientific communities. Our analysis is of interest not only to AI practitioners but also to other researchers and the general public as it offers ways to guide the emerging collaborations and interactions toward the most fruitful outcomes