ABSTRACT
Multi-center location of pharmaceutical logistics is the focus of pharmaceutical logistics research, and the dynamic uncertainty of pharmaceutical logistics multi-center location is a difficult point of research. In order to reduce the risk and cost of multi-enterprise, multi-category, large-volume, high-efficiency, and nationwide centralized medicine distribution, this study explores the best solution for planning medicine delivery for the medicine logistics. In this paper, based on the idea of big data, comprehensive consideration is given to uncertainties in center location, medicine type, medicine chemical characteristics, cost of medicine quality control (refrigeration and monitoring costs), delivery timeliness, and other factors. On this basis, a multi-center location- and route-optimization model for a medicine logistics company under dynamic uncertainty is constructed. The accuracy of the algorithm is improved by hybridizing the fuzzy C-means algorithm, sequential quadratic programming algorithm, and variable neighborhood search algorithm to combine the advantages of each. Finally, the model and the algorithm are verified through multi-enterprise, multi-category, high-volume, high-efficiency, and nationwide centralized medicine distribution cases, and various combinations of the three algorithms and several rival algorithms are compared and analyzed. Compared with rival algorithms, this hybrid algorithm has higher accuracy in solving multi-center location path optimization problem under the dynamic uncertainty in pharmaceutical logistics.
ABSTRACT
\Accurate and rapid detection of SARS-CoV-2 is significant for early tracing, isolating and treating the infected patients, which will efficiently prevent the virus large-scale spread from human to human. In this paper, two kinds of novel quantitative lateral flow test strip for N and RBD antigens of SARS-CoV-2 were established with high sensitivity, which utilize AIE luminogens (AIEgens) as reporter. Because of the high brightness and resistance of quenching property in aqueous of the AIEgens, the limit of detection of 7.2 ng/mL for N protein and 6.9 ng/mL for RBD protein could be achieved with the AIEgens-based lateral flow test strip. Furthermore, it was negative for other protein or antigen samples assay, which demonstrated the great specificity of the test strategy. A N95 mask equipped with the test strip was designed to employ as the antigen collector with excellent enrichment effect. Compared with the other two test strips based on the Au nanoparticle and FITC, the well-designed AIEgens-based lateral flow test strip presented high sensitivity and excellent anti-interference capacity in complex bio-samples. Furthermore, the AIEgens-based lateral flow test strip assay could be built as a promising platform for the emergency usage at pandemic.Funding: This work was supported by the NSFC (51961160730, 51873092, and 81921004), the National Key R&D Program of China (Intergovernmental Cooperation Project, 2017YFE0132200), the Fundamental Research Funds for the Central Universities, and the Tianjin Science Fund for Distinguished Young Scholars (19JCJQJC61200).Declaration of Interests: The authors declare no competing interests.