The role of data visualization in Railway Big Data Risk Analysis

Big Data Risk Analysis (BDRA) is one of the possible alleys for the further development of risk models in the railway transport. Big Data techniques allow a great quantity of information to be handled from different types of sources (e.g. unstructured text, signaling and train data). The benefits of this approach may lie in improving the understanding of the risk factors involved in railways, detecting possible new threats or assessing the risk levels for rolling stock, rail infrastructure or railway operations. For the efficient use of BDRA, the conversion of huge amounts of data into a simple and effective display is particularly challenging. Especially because it is presented to various specific target audiences. This work reports a literature review of risk communication and visualization in order to find out its applicability to BDRA, and beyond the visual techniques, what human factors have to be considered in the understanding and risk perception of the infor-mation when safety analysts and decision-makers start basing their decisions on BDRA analyses. It was found that BDRA requires different visualization strategies than those that have normally been carried out in risk analysis up to now

Windborne long-distance migration of malaria mosquitoes in the Sahel

Over the past two decades efforts to control malaria have halved the number of cases globally, yet burdens remain high in much of Africa and the elimination of malaria has not been achieved even in areas where extreme reductions have been sustained, such as South Africa1,2. Studies seeking to understand the paradoxical persistence of malaria in areas in which surface water is absent for 3–8 months of the year have suggested that some species of Anopheles mosquito use long-distance migration3. Here we confirm this hypothesis through aerial sampling of mosquitoes at 40–290 m above ground level and provide—to our knowledge—the first evidence of windborne migration of African malaria vectors, and consequently of the pathogens that they transmit. Ten species, including the primary malaria vector Anopheles coluzzii, were identified among 235 anopheline mosquitoes that were captured during 617 nocturnal aerial collections in the Sahel of Mali. Notably, females accounted for more than 80% of all of the mosquitoes that we collected. Of these, 90% had taken a blood meal before their migration, which implies that pathogens are probably transported over long distances by migrating females. The likelihood of capturing Anopheles species increased with altitude (the height of the sampling panel above ground level) and during the wet seasons, but variation between years and localities was minimal. Simulated trajectories of mosquito flights indicated that there would be mean nightly displacements of up to 300 km for 9-h flight durations. Annually, the estimated numbers of mosquitoes at altitude that cross a 100-km line perpendicular to the prevailing wind direction included 81,000 Anopheles gambiae sensu stricto, 6 million A. coluzzii and 44 million Anopheles squamosus. These results provide compelling evidence that millions of malaria vectors that have previously fed on blood frequently migrate over hundreds of kilometres, and thus almost certainly spread malaria over these distances. The successful elimination of malaria may therefore depend on whether the sources of migrant vectors can be identified and controlled

Successful breeding predicts divorce in plovers

When individuals breed more than once, parents are faced with the choice of whether to re-mate with their old partner or divorce and select a new mate. Evolutionary theory predicts that, following successful reproduction with a given partner, that partner should be retained for future reproduction. However, recent work in a polygamous bird, has instead indicated that successful parents divorced more often than failed breeders (Halimubieke et al. in Ecol Evol 9:10734–10745, 2019), because one parent can benefit by mating with a new partner and reproducing shortly after divorce. Here we investigate whether successful breeding predicts divorce using data from 14 well-monitored populations of plovers (Charadrius spp.). We show that successful nesting leads to divorce, whereas nest failure leads to retention of the mate for follow-up breeding. Plovers that divorced their partners and simultaneously deserted their broods produced more offspring within a season than parents that retained their mate. Our work provides a counterpoint to theoretical expectations that divorce is triggered by low reproductive success, and supports adaptive explanations of divorce as a strategy to improve individual reproductive success. In addition, we show that temperature may modulate these costs and benefits, and contribute to dynamic variation in patterns of divorce across plover breeding systems

Cover crops and weed suppression in the U.S. Midwest: A meta-analysis and modeling study

In addition to soil health and conservation benefits, cover crops (CCs) may offer weed control in the midwestern United States, but individual studies report vary- ing effects. We conducted a meta-analysis of studies measuring weed biomass (WBIO) or density (WDEN) in paired CC and no-cover treatments in corn (Zea mays L.)–soybean [Glycine max (L.) Merr] rotations in the U.S. Midwest. Fifteen studies provided 123 paired comparisons of WBIO and 119 of WDEN. Only grass CCs significantly reduced WBIO, while no CC reduced WDEN. We found no evidence CC management factors (e.g., termination method) directly affected out- comes. Our dataset showed that a 75% reduction in WBIO requires at least 5 Mg ha−1 of CC. Simulations from a process-based model (SALUS) indicated achieving 5 Mg ha−1 requires substantially earlier fall planting and later spring termination in most years, conflicting with typical cash-crop planting and harvesting. We conclude CCs significantly reduce WBIO, but current CC management constraints render these reductions variable and uncertain

Statistical Archetypal Analysis for Cognitive Categorization

Human knowledge develops through complex relationships between categories. In the era of Big Data, the concept of categorization implies data summa-rization in a limited number of well-separated groups that must be maximally and internally homogeneous at the same time. This proposal exploits archetypal analysis capabilities by finding a set of extreme points that can summarize entire data sets in homogeneous groups. The archetypes are then used to identify the best prototypes according to Rosch’s definition. Finally, in the geometric approach to cognitive science, the Voronoi tessellation based on the prototypes is used to define categorization. An example using a well-known wine dataset by Forina et al. illustrates the procedure