Kounis Syndrome Associated With Selective Anaphylaxis to Cefazolin.

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Stable propagation of pulsed beams in Kerr focusing media with modulated dispersion

We propose the modulation of dispersion to prevent collapse of planar pulsed beams which propagate in Kerr-type self-focusing optical media. As a result, we find a new type of two-dimensional spatio-temporal solitons stabilized by dispersion management. We have studied the existence and properties of these solitary waves both analytically and numerically. We show that the adequate choice of the modulation parameters optimizes the stabilization of the pulse.Comment: 3 pages, 3 figures, submitted to Optics Letter

Mozambique's Future: Modeling Population and Sustainable Development Challenges

What are the prospects for sustainable development over the next 20 years in Mozambique? Although it looks as if much of the development prospects are determined by such inherently unpredictable events as war, peace, and weather calamities, there are also many changes and patterns which have a long-term stability and which change only slowly over time. For example, socio-demographic changes, such as labor force skills, and population health have a long momentum. These are very important indicators for the economic development potential of a country. Also, although it is impossible to predict a particular year of heavy rains or droughts, there are long time series of weather from which we can calculate the country's vulnerability to single- or multiple-year weather disasters. To focus our efforts in answering this bold question, we concentrate on four issues: (1) Can poverty be erased in the next 20 years? (2) How will school enrollment lead to higher skills in the labor force by 2020? (3) What role will water play in development, in particular, water provision by rain to rural areas, and infrastructure to cities? (4) And, most importantly, what will be the impacts of the HIV/AIDS pandemic in the next decades

Machine learning-driven approach for large scale decision making with the analytic hierarchy process

The Analytic Hierarchy Process (AHP) multicriteria method can be cognitively demanding for large-scale decision problems due to the requirement for the decision maker to make pairwise evaluations of all alternatives. To address this issue, this paper presents an interactive method that uses online learning to provide scalability for AHP. The proposed method involves a machine learning algorithm that learns the decision maker’s preferences through evaluations of small subsets of solutions, and guides the search for the optimal solution. The methodology was tested on four optimization problems with different surfaces to validate the results. We conducted a one factor at a time experimentation of each hyperparameter implemented, such as the number of alternatives to query the decision maker, the learner method, and the strategies for solution selection and recommendation. The results demonstrate that the model is able to learn the utility function that characterizes the decision maker in approximately 15 iterations with only a few comparisons, resulting in significant time and cognitive effort savings. The initial subset of solutions can be chosen randomly or from a cluster. The subsequent ones are recommended during the iterative process, with the best selection strategy depending on the problem type. Recommendation based solely on the smallest Euclidean or Cosine distances reveals better results on linear problems. The proposed methodology can also easily incorporate new parameters and multicriteria methods based on pairwise comparisons.This research was funded by National Funds through the FCT—Portuguese Foundation for Science and Technology, References UIDB/05256/2020 and UIDP/05256/2020