Objective: To develop a synthetic process route and method more suitable for industrialscale-up production of FXIa inhibitor BAY2433334 (asundexian). Method: BAY2433334 is obtained from (2R)-2-aminobutyric acid by esterification, diazotization, condensation reaction, deacetyl deprotection, activation reaction, and Mitsunobu reaction. Results: The target compound BAY2433334 was synthesized by the above method is simple to operate, and the raw materials are inexpensive and readily available. Simultaneously, the product quality is very high: Few O-alkylated impurities are generated during the reaction, with a high N-alkylated product/O-alkylated product ratio (above 35-45:1). Meanwhile, the ee value is greater than 99%, which means that there are very few isomers present. As a result，no chiral resolution is required, which greatly reduces the cost. Conclusion: A new process route suitable for the industrial production of BAY2433334 has been developed in this paper, which avoids the patent limitations of the originator company of BAY2433334 to the preparation of BAY2433334, improves the product quality of BAY2433334, and reduces the production cost of BAY2433334.

Software project estimating (SPE) is a powerful but necessary step in the production of projects for software. Software project estimating includes several components, estimating software time, software resources, software costs, and software effort estimation (SEE). SEE seeks to estimate the number of "hours-person" and "months-person" of work required for developing or managing a software program. During the initial phase of the development of software, future efforts are impossible to predict. To estimate effort, many deep learning (DL) and machine learning (ML) models were previously created. For estimate, single-model techniques and ensemble methods were investigated in this research. Ensemble methods involve a combination of multiple separate models. Bagging, boosting, and voting were the ensemble methods looked into for estimation. Datasets considered for estimation were COCOMO81, COCOMONasa I, COCOMONasa II, Albrecht, Maxwell, China, Desharnais, Desharnais_1_1, Kitchenham, Boehm, and Belady. The evaluation criteria used MAE, RAE, RMSE, RRSE, and CC. The result from previous studies is that the total size of the set of data utilized is small in predicting the effort, as the largest size of the set of data used is the China dataset containing only 499 projects. So, in this study, the dataset has been merged to increase the total size of the set of data and increase the accuracy of prediction. The results indicated that the suggested merged dataset increased effort estimation accuracy.