A Schiff base ligand (L) was synthesized via condensation of N-( 1-naphthyl) ethylenediamine dihydrochloride with phthalaldehyde. The ligand was characterized by FT-IR, UV–Vis, 1H NMR, mass spectrometry, and elemental analysis (C, H, N). Five metal complexes (Co(II), Ni(II), Cu(II), Zn(II), and Cd(II)) were prepared with the ligand in a 1:1 (M:L) ratio using an aqueous ethanol solution. The complexes were characterized by FT-IR, UV–Vis, mass spectrometry, and elemental analysis (C, H, N). Additionally, 1H NMR spectroscopy was employed for Cd(II) complex. Antimicrobial activity of the ligand and its metal complexes against pathogenic bacteria (K. pneumoniae, E. coli, S. aureus, and S. epidermidis) and fungus (C. albicans) were evaluated

Advanced strategies for production forecasting, operational optimization, and decision-making enhancement have been employed through reservoir management and machine learning (ML) techniques. A hybrid model is established to predict future gas output in a gas reservoir through historical production data, including reservoir pressure, cumulative gas production, and cumulative water production for 67 months. The procedure starts with data preprocessing and applies seasonal exponential smoothing (SES) to capture seasonality and trends in production data, while an Artificial Neural Network (ANN) captures complicated spatiotemporal connections. The history replication in the models is quantified for accuracy through metric keys such as m