Abstract:We investigate numerically interactions between two in-phase or out-of-phase Airy beams and nonlinear accelerating beams in Kerr and saturable nonlinear media in one transverse dimension. We discuss different cases in which the beams with different intensities are launched into the medium, but accelerate in opposite directions. Since both the Airy beams and nonlinear accelerating beams possess infinite oscillating tails, we discuss interactions between truncated beams, with finite energies. During interactions we see solitons and soliton pairs generated that are not accelerating. In general, the higher the intensities of interacting beams, the easier to form solitons; when the intensities are small enough, no solitons are generated. Upon adjusting the interval between the launched beams, their interaction exhibits different properties. If the interval is large relative to the width of the first lobes, the generated soliton pairs just propagate individually and do not interact much. However, if the interval is comparable to the widths of the maximum lobes, the pairs strongly interact and display varied behavior.

BackgroundNeglected tropical diseases (NTDs) are closely related to poverty and affect over a billion people in developing countries. The unmet treatment needs cause high mortality and disability thereby imposing a huge burden with severe social and economic consequences. Although coordinated by the World Health Organization, various philanthropic organizations, national governments and the pharmaceutical industry have been making efforts in improving the situation, the control of NTDs is still inadequate and extremely difficult today. The lack of safe, effective and affordable medicines is a key contributing factor. This paper reviews the recent advances and some of the challenges that we are facing in the fight against NTDs.Main bodyIn recent years, a number of innovations have demonstrated propensity to promote drug discovery and development for NTDs. Implementation of multilateral collaborations leads to continued efforts and plays a crucial role in drug discovery. Proactive approaches and advanced technologies are urgently needed in drug innovation for NTDs. However, the control and elimination of NTDs remain a formidable task as it requires persistent international cooperation to make sustainable progresses for a long period of time. Some currently employed strategies were proposed and verified to be successful, which involve both mechanisms of ‘Push’ which aims at cutting the cost of research and development for industry and ‘Pull’ which aims at increasing market attractiveness. Coupled to this effort should be the exercise of shared responsibility globally to reduce risks, overcome obstacles and maximize benefits. Since NTDs are closely associated with poverty, it is absolutely essential that the stakeholders take concerted and long-term measures to meet multifaceted challenges by alleviating extreme poverty, strengthening social intervention, adapting climate changes, providing effective monitoring and ensuring timely delivery.ConclusionsThe ongoing endeavor at the global scale will ultimately benefit the patients, the countries they are living and, hopefully, the manufacturers who provide new preventive, diagnostic and therapeutic products.Electronic supplementary materialThe online version of this article (10.1186/s40249-018-0444-1) contains supplementary material, which is available to authorized users.

No abstract

This paper proposes a method for classification of incomplete data using neural network ensembles. In the method, the incomplete data set is analyzed and projected into a group of complete data subsets that give a full description of the known values in the data set by joining together. Those complete data subsets are then used as the training sets for the neural networks. Base classifiers are selected and integrated according to their classification accuracies and the support degrees of their training data sets to give the final predication. Compared with other methods dealing with missing data in classification, the proposed method can utilize all the information provided by the incomplete data, maintain maximum consistency of the incomplete data set and avoid the dependency on distribution or model assumptions. Experiments on two UCI datasets showed the superiority of the algorithm to other two typical treatments of missing data in ensemble learning.

20 BACKGROUND The COVID-19 epidemic, first emerged in Wuhan during December 2019, has 21 spread globally. While the mass population movement for Chinese New Year has significantly 22 influenced spreading the disease, little direct evidence exists about the relevance to epidemic and its 23 control of population movement from Wuhan, local emergency response, and medical resources in 24 China. fatality rate was 2.84%, much higher in Hubei than in other regions (3.27% vs 0.73%). The index of 29 population inflow from Hubei was positively correlated with total (Provincial r=0.9159, p<0.001; 30 City r=0.6311, p<0.001) and primary cases (Provincial r=0.8702, p<0.001; City r=0.6358, p<0.001). 31 The local health emergency measures (eg, city lockdown and traffic control) were associated with 32 reduced infections nationwide. Moreover, the number of public health employees per capita was 33 inversely correlated with total cases (r=-0.6295, p <0.001) and infection rates (r =-0.4912, p <0.01). 34 Similarly, cities with less medical resources had higher fatality (r =-0.4791, p<0.01) and lower cure 35 rates (r = 0.5286, p<0.01) among the confirmed cases. 36 CONCLUSIONS The spread of the COVID-19 in China in its early phase was attributed primarily 37 to population movement from Hubei, and effective governmental health emergency measures and 38 adequate medical resources played important roles in subsequent control of epidemic and improved 39 prognosis of affected individuals.40 41 .CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.(which was not peer-reviewed) The copyright holder for this preprint .