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This paper presents an accurate nonlinear classification method that can help physicians diagnose seizure in electroencephalographic (EEG) signal characterized by a disturbance in temporal and spectral content. This is accomplished by applying four steps. First, different EEG signals containing healthy, ictal and seizure-free (inter-ictal) activities are decomposed by empirical mode decomposition method. The instantaneous amplitudes and frequencies of resulted bands (intrinsic mode functions, IMF) are then tracked by the direct quadrature method (DQ). In contrast to other approaches, DQ cancels the effect of amplitude modulation on frequency calculation. The dissociation between instantaneous amplitude and frequency information is therefore fully achieved to avoid features confusion. Afterwards, the Shannon entropy values of both sets of instantaneous values (amplitudes and frequencies)—related to every IMF—are calculated. Finally, the obtained entropy values are classified by random forest tree. The proposed procedure yields 100% accuracy for (healthy)/(ictal) and 98.3–99.7% for (healthy)/(ictal)/(interictal) classification problems. The suggested method is hence robust, accurate, fast, user-friendly, data driven with open access interpretability.

The adsorption of phosphate ion onto natural reed (Arundo donax) was studied in this work. The effect of phosphate initial concentration, adsorbent dose, pH, temperature, and salt addition on adsorption uptake was investigated. The results showed that the adsorption uptake is directly proportional to the phosphate ion initial concentration and inversely proportional to the adsorbent's dose and temperature. A maximum adsorption capacity of 16.2 mg/g was observed at neutral pH. The addition of sodium and potassium chlorides has decreased the adsorption uptake. The adsorption isotherms agree better with the Langmuir model. The negative values of (ÁG) and (ÁH) obtained from the thermodynamic study, indicted that the adsorption process is spontaneous and exothermic. The experimental adsorption data were analyzed using three kinetic models: pseudo-first order, pseudo-second order, and intra-particle diffusion model. The pseudo-second-order model presented the best fit with a determination coefficient (R 2 ) higher than 0.99 and a minimum normalized standard deviation.

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This paper presents the results of electrochemical oxidation of dye-containing wastewater over a BDD anode. Batch experiments were conducted at a fixed current density of 2.8 mA/cm2 to analyze the performance of the electrochemical process for the treatment of textile and paint wastewater utilizing different supporting electrolytes (Na2SO4 and NaCl). During electrolysis, emphasis was put on measuring different parameters such as COD, turbidity, conductivity, and color removal. The results revealed that BDD cell exhibited higher COD removal efficiency for textile wastewater than for paint wastewater. Adding supporting electrolytes had a positive effect on COD, turbidity, and color removal efficiencies for both textile and paint industry effluents. For textile wastewater, Na2SO4 and NaCl yielded a reduction in COD of 94% in 6 hours compared to 84% with no electrolyte added. The presence of Na2SO4 and NaCl in paint wastewater resulted in different COD removal percentages of 71 and 85% respectively with 21% in raw sample after 4 hours of treatment. The discoloration reached a percentage higher than 96% for both effluents and for both electrolytes. Under the same experimental conditions, all cases showed turbidity removal higher than 97%. The kinetic study showed that the reaction rate followed pseudo-first-order kinetics.