Protein IDO1 (indoleamine 2,3-dioxygenase) occupies a critical position in the regulation of the immune system and is involved in cancer progression and the development of immune diseases. Being a therapeutic target for such critical diseases, we aimed to investigate the IDO1 inhibition activity of thirty-nine triazole derivatives using a quantitative structure-activity relationship. The dataset was under principal component analysis, multiple linear regression, and multiple non-linear regression from which two models were generated. The best 2D-QSAR model was generated using linear regression, demonstrating a determination coefficient of R2=0.680, a good acceptable internal cross-validated coefficient of R2cv=0.700, an error of MSE=0.074, and a good predictive potential of R2test=0.809. The QSAR model was further investigated using the applicability domain, which showed that all molecules were within the applicability domain, hence the absence of an outlier. Overall, the obtained results provide a reliable and highly predictive model for the design and prediction of new IDO1 inhibitors thereby influencing cancer progression and autoimmune disease development.

Machine vision systems are among the novel tools proven to be useful in different applications, among which monitoring and controlling manufacturing processes is one of the most important ones. However, due to the complexity resulted from high-dimensional image data and their inherent correlations, the acquisition of traditional statistical process control tools seems inapplicable. To overcome the shortcomings of the traditional methods in this regard, a statistical model is proposed in this paper which utilizes the concepts of both the PCA-based T2 control chart and the classification methods to develop a tool capable of controlling an image-based process. By defining the warning zones, collected data taken from an image-based process are classified into more than the two classes related to in-control and out-of-control processes. This helps practitioners to define rules to make it easier to realize when the process is getting out of control. Through simulation, the accuracy performance and the speed of four different types of classifiers including linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), kth nearest neighbors (KNN), and support vector machine (SVM) are assessed in different scenarios, based on which the functionality of the proposed approach is evaluated in in-control and out-of-control conditions.

Multi-criteria decision making approach is one of the most troublesome tools for solving the tangled optimization problems in the machining area due to its capability of solving the complex optimization problems in the production process. Turning is widely used in the manufacturing processes as it offers enormous advantages like good quality product, customer satisfaction, economical and relatively easy to apply. A contemporary approach, MOORA coupled with PCA, was used to ascertain an optimal combination of input parameters (spindle speed, depth of cut and feed rate) for the given output parameters (power consumption, average surface roughness and frequency of tool vibration) using L27 orthogonal array for turning on ASTM A588 mild steel. Comparison between MOORA-PCA and TOPSIS-PCA shows the effectiveness of MOORA over TOPSIS method. The optimum parameter combination for multi-performance characteristics has been established for ASTM A588 mild steel are spindle speed 160 rpm, depth of cut 0.1 mm and feed rate 0.08 mm/rev. Therefore, this study focuses on the application of the hybrid MCDM approach as a vital selection making tool to deal with multi objective optimization problems.

Experimental and computational approaches were used to estimate the lipophilicity of novel 1,2,4-triazole derivatives. These derivatives have been subjected to this research, because they exhibit antimicrobial activity. The chromatographic analysis of RP-HPLC and RP-TLC was carried out using methanol-water or acetonitrile-water as mobile phase. The linear relationships between logk (or R_M) values and the concentration of organic modifier were obtained. The lipophilicity was expressed as chromatographically derived descriptors: ?logk?_W, S, ?0 and scores logk and R_Mcorresponding to the first principal component. The experimental lipophilicity data have been compared with the computer calculated lipophilicity parameters (milogP, clogP, ALOGPs, AClogP, AlogP, MLOGP, KOWWIN, XLOGP2, XLOGP3, ?logP?_ChS) of the same molecules. The matrices were created with ?logk?_W or R_M^0and logP and they have been the subject of PCA analysis.

Personnel selection is one of the most important problems that organizations have to deal with. Competent personnel is one of the key factors for the success of organizations. Project manager selection due to special requirements is significantly important. A project manager must have the ability of managing costs, time and resources through the optimistic way. Furthermore he/she has to own general management skills and benefit from adequate information about the project context. Project managers in petroleum industry carry very important duties than other project managers. In this research, we try to develop a model in order to select a project manager for pe-troleum industry. The proposed model is based on multi criteria decision making and a statistical method named principle component analysis (PCA). The methodology considers all of the im-portant criteria and benefit from an experienced expert panel in order to extract the weights of the criteria. Also a numerical example demonstrates the function of the model and is verified by VI-KOR method.