Two series of bent and liner core mesogen containing 1,2,4-traizole ring [VI]a,g and series were synthesized by many steps starting from esterification of isophthalic acid and terephathalic acid with methanol to yield diester compound [I]a,b which was converted to their acid hydrazide [II]a,b and the acid hydrazide reacted with ammonium thiocyanate or diester reacted with thiosemicarbazide to yield compounds [III]a,b. Then cyclization by 4% NaOH to yielded 1,2,4 traizole-3- thiol compounds [IV]a,b , afterword adding hydrazine hydrate to yield compounds [V]a,b. These compounds condensated with different substituted aldehyde to give new Schiff bases[VI]a,b ,[VII]a,b . Also , reaction acid hydrazide [II]a,b with aldehyde [VII] to yielded Schif

Bauxite residue (red mud) is a waste material from alumina refineries in the Bayer process, containing significant quantities of valuable metals, notably scandium (Sc). The objective of this study is to recover Sc (III) from Hungarian bauxite residue by using hydrometallurgical processes, including solvent extraction and leaching. Red mud directly leached with hydrochloric acid to generate the leachate solution. The significant iron content (~38 %) in red mud makes it hard to recover scandium selectively due to comparable physicochemical characteristics. According to the findings, Fe (III) could be effectively extracted from hydrochloric acid leachate as HFeC14 using diethyl ether before Sc extraction. Protocol B demonstrated superior recov

Bauxite residue (red mud) is a waste material from alumina refineries in the Bayer process, containing significant quantities of valuable metals, notably scandium (Sc). The objective of this study is to recover Sc (III) from Hungarian bauxite residue by using hydrometallurgical processes, including solvent extraction and leaching. Red mud directly leached with hydrochloric acid to generate the leachate solution. The significant iron content (~38 %) in red mud makes it hard to recover scandium selectively due to comparable physicochemical characteristics. According to the findings, Fe (III) could be effectively extracted from hydrochloric acid leachate as HFeC14 using diethyl ether before Sc extraction. Protocol B demonstrated superior recov

New metal complexes of some transition metal ions Co(II), Cu(II) , Cd(II) and Zn(II) were prepared by their reaction with previously prepared ligands HLI= (P-methyl anilino) phenyl acetonitrile and HLII = (P-methyl anilino) –P– chloro phenyl acetonitrile . The two ligands were prepared by Strecker’s procedure which includ the reaction of p- toluidine with benzaldehyde and P- chlorobenzaldehyde respectively. Structures were proposed depending on atomic absorption , i.r. and u.v.visible spectra in addition to magnetic susceptibility and electrical conductivity measurements.

The present work aimed to study the efficiency of nanofiltration (NF) and reverse osmosis (RO) membrane for heavy metal removal from wastewater and study the factors affecting the performance of these two membranes: feed concentrations for heavy metal ions, pressure, and flow rate. The experimental results showed, heavy metals concentration in permeate increase with raise in feed concentrations, decline with increase in flow rate. The raise of pressure, heavy metals concentration decreases for RO membrane, but for NF membrane the concentration decrease and then at high pressure increase. The rejection percentage for chromium in NF and RO is 99.7% and 99.9%, for copper is 98.4% and 99.3%, for zinc is 97.9% and 99.5%, for nickel is 97.2% and

Beyond the immediate content of speech, the voice can provide rich information about a speaker's demographics, including age and gender. Estimating a speaker's age and gender offers a wide range of applications, spanning from voice forensic analysis to personalized advertising, healthcare monitoring, and human-computer interaction. However, pinpointing precise age remains intricate due to age ambiguity. Specifically, utterances from individuals at adjacent ages are frequently indistinguishable. Addressing this, we propose a novel, end-to-end approach that deploys Mozilla's Common Voice dataset to transform raw audio into high-quality feature representations using Wav2Vec2.0 embeddings. These are then channeled into our self-attentio