The dynamic development of computer and software technology in recent years was accompanied by the expansion and widespread implementation of artificial intelligence (AI) based methods in many aspects of human life. A prominent field where rapid progress was observed are high‐throughput methods in biology that generate big amounts of data that need to be processed and analyzed. Therefore, AI methods are more and more applied in the biomedical field, among others for RNA‐protein binding sites prediction, DNA sequence function prediction, protein‐protein interaction prediction, or biomedical image classification. Stem cells are widely used in biomedical research, e.g., leukemia or other disease studies. Our proposed approach of

Removal of heavy metals from waste water has received a great deal of attention. The compare Cr
(VI) adsorption characteristics removing from wastewater by using thermally modified and non-modified
eggshells were examined

(3) (PDF) Theoretical investigation of charge transfer at N3 sensitized molecule dye contact with TiO2 and ZnO semiconductor. Available from: https://www.researchgate.net/publication/362773606_Theoretical_investigation_of_charge_transfer_at_N3_sensitized_molecule_dye_contact_with_TiO2_and_ZnO_semiconductor [accessed May 01 2023].

The influence of pre- shot peening and welding parameters on mechanical and metallurgical properties of dissimilar and similar aluminum alloys AA2024-T3 and AA6061-T6 joints using friction stir welding have been studied. In this work, numbers of plates were equipped from sheet alloys in dimensions (150*50*6) mm then some of them were exposed to shot peening process before friction stir welding using steel ball having diameter 1.25 mm for period of 15 minutes. FSW joints were manufactured from plates at three welding speeds (28, 40, 56 mm/min) and welding speed 40mm/min was chosen at a rotating speed of 1400 rpm for welding the dissimilar pre- shot plates. Tow joints were made at rotational speed of 1000 rpm and welding speed of 40m/min f

The current study included the separation of three alkaloid compounds from Anastatica Hierochuntica and studied the effect of the these compounds on cancerous cells , specifically liver cancer it was found that compound number one is the most influential or inhibiting at 50 percent followed by compound number three when using concentration of 400 μg/mL.

Upper limb amputation is a condition that severely limits the amputee’s movement. Patients who have lost the use of one or more of their upper extremities have difficulty performing activities of daily living. To help improve the control of upper limb prosthesis with pattern recognition, non-invasive approaches (EEG and EMG signals) is proposed in this paper and are integrated with machine learning techniques to recognize the upper-limb motions of subjects. EMG and EEG signals are combined, and five features are utilized to classify seven hand movements such as (wrist flexion (WF), outward part of the wrist (WE), hand open (HO), hand close (HC), pronation (PRO), supination (SUP), and rest (RST)). Experiments demonstrate that usin

The corrosion of carbon steel in single phase (water with 0.1N NaCl ) and two immiscible phases (kerosene-water) using turbulently agitated system is investigated. The experiments are carried out for Reynolds number (Re) range of 38000 to 95000 corresponding  to rotational velocities from 600 to 1400 rpm using circular disk turbine agitator at 40 0C. In two-phase system test runs are carried out in aqueous phase (water) concentrations of 1 % vol., 5 % vol., 8% vol., and 16% vol. mixed with kerosene at various Re. The effect of Reynolds number (Re), percent of dispersed phase, dispersed drops diameter, and number of drops per unit volume on the corrosion rate is investigated and discussed. Test runs are carried out using two types of

The turning process has various factors, which affecting machinability and should be investigated. These are surface roughness, tool life, power consumption, cutting temperature, machining force components, tool wear, and chip thickness ratio. These factors made the process nonlinear and complicated. This work aims to build neural network models to correlate the cutting parameters, namely cutting speed, depth of cut and feed rate, to the machining force and chip thickness ratio. The turning process was performed on high strength aluminum alloy 7075-T6. Three radial basis neural networks are constructed for cutting force, passive force, and feed force. In addition, a radial basis network is constructed to model the chip thickness ratio. T