The Ostruthin was extracted and identified its structure from rhizomes of Luvunga scandens located at Ta Cu Mountain, Binh Thuan province, Vietnam for the first time. Gold nanoparticles (AuNPs) were synthesized by the green method. The AuNPs acted as antitumor against breast cancer cell line (MCF-7), human liver cancer cell line (HepG2), and Non-Small Cell Lung (NCI-H460). They showed the potential antitumor activity against MCF-7 with the IC50 value of 65.47�3.09 �M. The antitumor activity of the AuNPs was also compared with the extracted constituents from the root of Luvunga Scandens in a previous article. The AuNPs were exposed to high antitumor activity against MCF-7 and Hep G2, human cancer cell lines. The AuNPs have also been tested the antibacterial activity and shown the moderate antibacterial activity on both Salmonella enterica and Bacillus subtilis at a concentration of 0.25 mM.

In this study, the stability constants (logβ11) of twenty-eight new complexes between several ion metals and thiosemicarbazone ligands were predicted on the basis of the quantitative structure property relationship (QSPR) modeling. The stability constants were calculated from the results of the QSPR models. The QSPR models were built by using the multivariate least regression (QSPRMLR) and artificial neural network (QSPRANN). The molecular descriptors, physicochemical and quantum descriptors of complexes were generated from molecular geometric structure and semi-empirical quantum calculation PM7 and PM7/sparkle. The best linear model QSPRMLR involves five descriptors, namely Total energy, xch6, xp10, SdsN, and Maxneg. The quality of the QSPRMLR model was validated by the statistical values that were R2train = 0.860, Q2LOO = 0.799, SE = 1.242, Fstat = 54.14 and PRESS = 97.46. The neural network model QSPRANN with architecture I(5)-HL(9)-O(1) was presented with the statistical values: R2train = 0.8322, Q2CV = 0.9935 and Q2test = 0.9105. Also, the QSPR models were evaluated externally and achieved good performance results with those from the experimental literature. In addition, the results from the QSPR models could be used to predict the stability constants of other new metal-thiosemicarbazones.

Currently, many drugs are being studied and potentially used in the treatment of SARS-CoV-2. Compounds studied are mostly organic substances. This work investigates the ability to inhibit SARS-CoV-2 of various 20 metal ions based on their ability to inhibit several biological systems; the physicochemical properties of metal ions were calculated by quantum chemistry DFT (B3LYP/ LanL2DZ) were used to develop the QIPAR hybrid models. Hybrid models QIPARGA-MLR (k = 4) and QIPARGA-ANN with architecture I(4)-HL(9)-O(1) were developed to predict the biological activity of metal ions. Metal ions were also investigated for their inhibitory potential for the protein SARS-CoV-2 (PDB6LU7) by docking simulation techniques. We predicted the binding sites of metal ions to the active sites of the SARS-CoV-2 protein (PDB6LU7). These studies are consistent with their activities against different biological systems. This research will also contribute to the development of metal oxide nanomaterials.

Construction cost is considered as one of the most important criteria in making decision for investment, especially in the idea formation stage. The paper provides a tool to determine the construction cost of the school during the conceptual design phase, when project information is still sketchy, not detailed yet. This research use artificial neural network techniques for estimating the construction schools cost. The model is based on the weight which set by the excel algorithm and the weight optimization method of the error back propagation. The excel algorithm is tested and optimized by using Statistical Package for the Social Sciences software. The basic model achieves accuracy 90.1% and the optimal model achieves 96.6% accuracy optimization. The optimal weight is used to build the cost estimation model with the automatic calculation program by the excel spreadsheet, which allows for updating the data value limited, updating weight when were adjusted.

The stability constants (logβ11) of forty-two new metal-thiosemicarbazone complexes were predicted based on the results of the quantitative structure-property relationship (QSPR). The QSPR models were developed from 88 logb11 values of experimental complexes by using the multivariate linear regression (QSPRMLR) and artificial neural network (QSPRANN). Four descriptors such as xch9, xv0, core-core repulsion and cosmo area were found out in the best of the linear model QSPRMLR which was harshly evaluated by the statistical values: R2train = 0.864, Q2LOO = 0.840, SE = 0.711, Fstat = 131,355 and PRESS = 49.31. Furthermore, the artificial neural network model QSPRANN with architecture I(4)-HL(5)-O(1) was discovered with the same variables of the QSPRMLR model that the statistical results were extremely impressive as R2train = 0.970, Q2CV = 0.984 and Q2test = 0.974. Also, both of the QSPR models were externally validated on the data set of 18 logb11 values of independently experimental complexes. As a consequence, the results from the QSPR models could be used to calculate the stability constants of other new metal-thiosemicarbazones.