We study the consequences of the existence of a one-parameter group of conformal motions for anisotropic matter, in the context of general relativity. It is shown that for a class of conformal motions (special conformal motions), the equation of state is uniquely determined by the Einstein equations. For spherically symmetric and static distributions of matter we found two analytical solutions of the Einstein equations which correspond to isotropic and anisotropic matter, respectively. Both solutions can be matched to the Schwarzschild exterior metric and possesses positive energy density larger than the stresses, everywhere within the sphere.

A general method to obtain models of nonstatic radiating fluid spheres is given. Through a certain parameter the models are continuously connected to static solutions of Einstein's equations. The procedure leads, with a minimum of additional hypothesis, to a system of three first-order differential equations for quantities evaluated at the surface of the sphere. An integration of this system allows one at once to obtain the profile of the physical variables inside the sphere. A criterion to predict the bounce of the surface appears in a natural way. As illustrations, we have integrated numerically the equations for two different models: The first is derived from the Schwarzschild interior solution and the second from Tolman's solution VI. In the first case bouncing of the surface is impossible but in the second model the bouncing may occur if certain conditions are satisfied.

An alternative forecasting technique for chaotic time series, based on the optimal association concept, is presented. The method is applied on series generated by the logistic and Hénon maps, and on experimental data corresponding to rainfall of a storm event. In all three cases the quality of the forecasts is analyzed in terms of the prediction interval, the length of the historic data available on the time series, and the dimension of the embedding space. It is shown that the method is capable of producing very satisfactory short-term forecasts for data sequences of small lengths as they often occur in real experiments. Our results also show that the present technique can be used to discriminate complex signals associated with deterministic chaos from those of random origin.

Differential diagnosis between bacterial and viral meningitis is crucial. In our study, to differentiate bacterial vs. viral meningitis, three machine learning (ML) algorithms (multiple logistic regression (MLR), random forest (RF), and naïve-Bayes (NB)) were applied for the two age groups (0–14 and >14 years) of patients with meningitis by both conventional (culture) and molecular (PCR) methods. Cerebrospinal fluid (CSF) neutrophils, CSF lymphocytes, neutrophil-to-lymphocyte ratio (NLR), blood albumin, blood C-reactive protein (CRP), glucose, blood soluble urokinase-type plasminogen activator receptor (suPAR), and CSF lymphocytes-to-blood CRP ratio (LCR) were used as predictors for the ML algorithms. The performance of the ML algorithms was evaluated through a cross-validation procedure, and optimal predictions of the type of meningitis were above 95% for viral and 78% for bacterial meningitis. Overall, MLR and RF yielded the best performance when using CSF neutrophils, CSF lymphocytes, NLR, albumin, glucose, gender, and CRP. Also, our results reconfirm the high diagnostic accuracy of NLR in the differential diagnosis between bacterial and viral meningitis.

Networks of chaotic coupled maps are considered as string and language generators. It is shown that such networks can be used as encrypting systems where the ciphertext contains information about the evolution of the network and also about the way to select the plaintext symbols from the string associated to the network evolution. The secret key provides the network parameters, such as the coupling strengths.Comment: 5 pp, 3 figs, accepted in Phys. Rev. E Rapid Communications (2002