An artificial immune system for fuzzy-rule induction in data mining

This work proposes a classification-rule discovery algorithm integrating artificial immune systems and fuzzy systems. The algorithm consists of two parts: a sequential covering procedure and a rule evolution procedure. Each antibody (candidate solution) corresponds to a classification rule. The classification of new examples (antigens) considers not only the fitness of a fuzzy rule based on the entire training set, but also the affinity between the rule and the new example. This affinity must be greater than a threshold in order for the fuzzy rule to be activated, and it is proposed an adaptive procedure for computing this threshold for each rule. This paper reports results for the proposed algorithm in several data sets. Results are analyzed with respect to both predictive accuracy and rule set simplicity, and are compared with C4.5rules, a very popular data mining algorithm

Possibilistic KNN regression using tolerance intervals

International audienceBy employing regression methods minimizing predictive risk, we are usually looking for precise values which tends to their true response value. However, in some situations, it may be more reasonable to predict intervals rather than precise values. In this paper, we focus to find such intervals for the K-nearest neighbors (KNN) method with precise values for inputs and output. In KNN, the prediction intervals are usually built by considering the local probability distribution of the neighborhood. In situations where we do not dispose of enough data in the neighborhood to obtain statistically significant distributions, we would rather wish to build intervals which takes into account such distribution uncertainties. For this latter we suggest to use tolerance intervals to build the maximal specific possibility distribution that bounds each population quantiles of the true distribution (with a fixed confidence level) that might have generated our sample set. Next we propose a new interval regression method based on KNN which take advantage of our possibility distribution in order to choose, for each instance, the value of K which will be a good trade-off between precision and uncertainty due to the limited sample size. Finally we apply our method on an aircraft trajectory prediction problem

COVID-19 among patients with orthopedic surgery: our experience from the Middle East

Background: We report our experiences with COVID-19 in one of the largest referral orthopedic centers in the Middle East and aimed to describe the epidemiology and clinical characteristics of these patients. Methods: During February 20 and April 20, 2020, patients who underwent orthopedic surgery and healthcare staff who were in contact with these patients were screened for COVID-19. To identify patients who were in the incubation period of COVID-19 during their hospital stay, all patients were tested again for COVID-19 4 weeks after discharge. Results: Overall, 1244 patients underwent orthopedic surgery (1123 emergency and 121 elective) during the study period. Overall, 17 patients were diagnosed with COVID-19 during hospital admission and seven after discharge. Among the total 24 patients with COVID-19, 15 were (62.5) males with a mean (SD) age of 47.0±1.6 years old. Emergency surgeries were performed in 20 (83.3) patients, and elective surgery was done in the remaining 4 patients which included one case of posterior spinal fusion, spondylolisthesis, acromioclavicular joint dislocation, and one case of leg necrosis. A considerable number of infections occurred in patients with intertrochanteric fractures (n=7, 29.2), followed by pelvic fractures (n=2, 8.3), humerus fractures (n=2, 8.3), and tibial plateau fractures (n=2, 8.3). Fever (n=11, 45.8) and cough (n=10, 37.5) were the most common symptoms among patients. Laboratory examinations showed leukopenia in 2 patients (8.3) and lymphopenia in 4 (16.7) patients. One patient with a history of cancer died 2 weeks after discharge due to myocardial infarction. Among hospital staff, 26 individuals contracted COVID-19 during the study period, which included 13 (50) males. Physicians were the most commonly infected group (n = 11), followed by operation room technicians (n = 5), nurses (n = 4), and paramedics (n = 4). Conclusions: Patients who undergo surgical treatment for orthopedic problems, particularly lower limb fractures with limited ambulation, are at a higher risk of acquiring COVID-19 infections, although they may not be at higher risks for death compared to the general population. Orthopedic surgeons in particular and other hospital staff who are in close contact with these patients must be adequately trained and given appropriate personal protective equipment during the COVID-19 outbreak. © 2021, The Author(s)

Eigenmodes and growth rates of relativistic current filamentation instability in a collisional plasma

I theoretically found eigenmodes and growth rates of relativistic current filamentation instability in collisional regimes, deriving a generalized dispersion relation from self-consistent beam-Maxwell equations. For symmetrically counterstreaming, fully relativistic electron currents, the collisional coupling between electrons and ions creates the unstable modes of growing oscillation and wave, which stand out for long-wavelength perturbations. In the stronger collisional regime, the growing oscillatory mode tends to be dominant for all wavelengths. In the collisionless limit, those modes vanish, while maintaining another purely growing mode that exactly coincides with a standard relativistic Weibel mode. It is also shown that the effects of electron-electron collisions and thermal spread lower the growth rate of the relativistic Weibel instability. The present mechanisms of filamentation dynamics are essential for transport of homogeneous electron beam produced by the interaction of high power laser pulses with plasma.Comment: 44 pages, 12 figures. Accepted for publication in Phys. Rev.

The Physics of Star Cluster Formation and Evolution

© 2020 Springer-Verlag. The final publication is available at Springer via https://doi.org/10.1007/s11214-020-00689-4.Star clusters form in dense, hierarchically collapsing gas clouds. Bulk kinetic energy is transformed to turbulence with stars forming from cores fed by filaments. In the most compact regions, stellar feedback is least effective in removing the gas and stars may form very efficiently. These are also the regions where, in high-mass clusters, ejecta from some kind of high-mass stars are effectively captured during the formation phase of some of the low mass stars and effectively channeled into the latter to form multiple populations. Star formation epochs in star clusters are generally set by gas flows that determine the abundance of gas in the cluster. We argue that there is likely only one star formation epoch after which clusters remain essentially clear of gas by cluster winds. Collisional dynamics is important in this phase leading to core collapse, expansion and eventual dispersion of every cluster. We review recent developments in the field with a focus on theoretical work.Peer reviewe