Sensibly highlighting the hidden structures of many real-world networks has attracted growing interest and triggered a vast array of techniques on what is called nowadays community detection (CD) problem. Non-deterministic metaheuristics are proved to competitively transcending the limits of the counterpart deterministic heuristics in solving community detection problem. Despite the increasing interest, most of the existing metaheuristic based community detection (MCD) algorithms reflect one traditional language. Generally, they tend to explicitly project some features of real communities into different definitions of single or multi-objective optimization functions. The design of other operators, however, remains canonical lacking any inte

With the aim of developing potential antimicrobials, a series of novel Ciprofloxacin methylene isatin derivatives incorporating different aromatic aldehydes were synthesized and characterized by FTIR, 1H NMR, Mass spectroscopy and bases of elemental analysis. In addition, the in vitro antibacterial and antifungal properties were tested against some human pathogenic microorganisms by employing the disc diffusion technique. A majority of compounds were showing activity against several of the microorganisms. The relationship between the functional group variation and the biological activity of the evaluated compounds is discussed. From comparisons of the compounds, 3c was determined to be the most active compound.

Healthcare professionals routinely use audio signals, generated by the human body, to help diagnose disease or assess its progression. With new technologies, it is now possible to collect human-generated sounds, such as coughing. Audio-based machine learning technologies can be adopted for automatic analysis of collected data. Valuable and rich information can be obtained from the cough signal and extracting effective characteristics from a finite duration time interval that changes as a function of time. This article presents a proposed approach to the detection and diagnosis of COVID-19 through the processing of cough collected from patients suffering from the most common symptoms of this pandemic. The proposed method is based on adopt