Accurate definition of control strategies using cross validated stepwise regression and Monte Carlo simulation

A Comparative Analysis of Technical Efficiency and Profitability of Agribusiness and Non-Agribusiness Enterprises in Eastern DRC

The purpose of this study was to determine whether agribusiness could be competitive compared to non-agribusiness employment opportunities in terms of technical efficiency and profitability. We used data collected on all seven operating cassava community processing centers (CCPCs) and 150 comparable non-agribusiness enterprises in South Kivu province. A Data Envelopment Analysis (DEA), as well as cost–benefit ratios and net monthly revenue, were used to examine technical efficiency and profitability. Our results showed that agribusiness was more competitive than non-agribusiness in terms of technical efficiency and profitability. The cost–benefit ratio shows that every dollar invested in agribusiness earns investors US $2.8, while it earns investors in non-agribusiness US $2.1. Moreover, technical efficiency increases significantly with agribusiness. These results show that agribusiness can compete with other non-agribusiness activities, and it remains a solution to youth unemployment in the region.

Poplar's Waterlogging Resistance Modeling and Evaluating: Exploring and Perfecting the Feasibility of Machine Learning Methods in Plant Science

Floods, as one of the most common disasters in the natural environment, have caused huge losses to human life and property. Predicting the flood resistance of poplar can effectively help researchers select seedlings scientifically and resist floods precisely. Using machine learning algorithms, models of poplar's waterlogging tolerance were established and evaluated. First of all, the evaluation indexes of poplar's waterlogging tolerance were analyzed and determined. Then, significance testing, correlation analysis, and three feature selection algorithms (Hierarchical clustering, Lasso, and Stepwise regression) were used to screen photosynthesis, chlorophyll fluorescence, and environmental parameters. Based on this, four machine learning methods, BP neural network regression (BPR), extreme learning machine regression (ELMR), support vector regression (SVR), and random forest regression (RFR) were used to predict the flood resistance of poplar. The results show that random forest regression (RFR) and support vector regression (SVR) have high precision. On the test set, the coefficient of determination (R²) is 0.8351 and 0.6864, the root mean square error (RMSE) is 0.2016 and 0.2780, and the mean absolute error (MAE) is 0.1782 and 0.2031, respectively. Therefore, random forest regression (RFR) and support vector regression (SVR) can be given priority to predict poplar flood resistance.

A novel approach to residence time distribution characterization in a mAb continuous process

Residence time distribution modeling of integrated perfusion to capture process can elucidate the impact of product quality excursions and filter fouling on monoclonal antibody production. In this case study, a glycosylation inhibitor and fluorescently labeled antibody are applied to the continuous process to study protein quality modulation, perfusion filter fouling, and unit operation hold times. The unit operations were modeled as continuous-stirred tank reactors and the residence time distribution of a small molecule glycan inhibitor and impact on glycosylation were characterized. A fluorescently labeled antibody was applied as a tracer molecule and confirmed the impact of packed cell volume and filter fouling. This study demonstrates how a biologics continuous process can be modeled and characterized through residence time distribution to ensure a robust, well-understood process.