“线上线下-虚实结合”在有机化学实验教学中的应用探索——以正溴丁烷的制备实验为例

In response to the impact of the Covid-19 epidemic on organic chemistry laboratory teaching, this article analyzes and discusses the current limitation of traditional organic chemistry laboratory teaching. Taking the "preparation of n-bromobutane" as an example, a new "online-offline and virtual-actual combination" teaching mode with the combination of "Tencent Meeting, MLabs, laboratory and WeChat group" is introduced. The practice of the new mode of teaching includes the following steps: pre-class online guiding by teacher, student preview and practice based on the virtual simulation platform, students and teacher discussion and operation in laboratory, and post-classcomprehensive assessment and Q&A", and good teaching effects have been received. It is significant in cultivating and improving the independent innovation and practical ability, and provides reference for the organic chemistry laboratory teaching reform and also lays a foundation for the construction of "outstanding course" of organic chemistry.     

Green chemistry and sustainability metrics in the pharmaceutical manufacturing sector

1. Download : Download high-res image (202KB)
2. Download : Download full-size image

     

Bipartite flocking for multi-agent systems

This paper addresses the bipartite flock control problem where a multi-agent system splits into two clusters upon internal or external excitations. Using structurally balanced signed graph theory, LaSalle’s invariance principle and Barbalat’s Lemma, we prove that the proposed algorithm guarantees a bipartite flocking behavior. In each of the two disjoint clusters, all individuals move with the same direction. Meanwhile, every pair of agents in different clusters moves with opposite directions. Moreover, all agents in the two separated clusters approach a common velocity magnitude, and collision avoidance among all agents is ensured as well. Finally, the proposed bipartite flock control method is examined by numerical simulations. The bipartite flocking motion addressed by this paper has its references in both natural collective motions and human group behaviors such as predator–prey and panic escaping scenarios.     

Simple laser vision sensor calibration for surface profiling applications

Due to the relatively large structures in the Oil and Gas industry, original equipment manufacturers (OEMs) have been implementing custom-designed laser vision sensor (LVS) surface profiling systems as part of quality control in their manufacturing processes. The rough manufacturing environment and the continuous movement and misalignment of these custom-designed tools adversely affect the accuracy of laser-based vision surface profiling applications. Accordingly, Oil and Gas businesses have been raising the demand from the OEMs to implement practical and robust LVS calibration techniques prior to running any visual inspections. This effort introduces an LVS calibration technique representing a simplified version of two known calibration techniques, which are commonly implemented to obtain a calibrated LVS system for surface profiling applications. Both calibration techniques are implemented virtually and experimentally to scan simulated and three-dimensional (3D) printed features of known profiles, respectively. Scanned data is transformed from the camera frame to points in the world coordinate system and compared with the input profiles to validate the introduced calibration technique capability against the more complex approach and preliminarily assess the measurement technique for weld profiling applications. Moreover, the sensitivity to stand-off distances is analyzed to illustrate the practicality of the presented technique.     

Supplier quality improvement: The value of information under uncertainty

We consider supplier development decisions for prime manufacturers with extensive supply bases producing complex, highly engineered products. We propose a novel modelling approach to support supply chain managers decide the optimal level of investment to improve quality performance under uncertainty. We develop a Poisson–Gamma model within a Bayesian framework, representing both the epistemic and aleatory uncertainties in non-conformance rates. Estimates are obtained to value a supplier quality improvement activity and assess if it is worth gaining more information to reduce epistemic uncertainty. The theoretical properties of our model provide new insights about the relationship between the degree of epistemic uncertainty, the effectiveness of development programmes, and the levels of investment. We find that the optimal level of investment does not have a monotonic relationship with the rate of effectiveness. If investment is deferred until epistemic uncertainty is removed then the expected optimal investment monotonically decreases as prior variance increases but only if the prior mean is above a critical threshold. We develop methods to facilitate practical application of the model to industrial decisions by a) enabling use of the model with typical data available to major companies and b) developing computationally efficient approximations that can be implemented easily. Application to a real industry context illustrates the use of the model to support practical planning decisions to learn more about supplier quality and to invest in improving supplier capability.     

The (S−1,S) inventory model and its counterparts in queueing theory

We explore the relationship between the ($S?1,S$) inventory model and three well-known queueing models: the Erlang loss system, the machine-repair model and a two-node Jackson network. Exploiting this relationship allows us to obtain key performance measures of the ($S?1,S$) model, like the so-called virtual outdating time, the number of items on the shelf in steady state, the long-run rate of unsatisfied demands and the distribution of the empty shelf period.     

An evaluation of semidefinite programming based approaches for discrete lot-sizing problems

The present work is intended as a first step towards applying semidefinite programming models and tools to discrete lot-sizing problems including sequence-dependent changeover costs and times. Such problems can be formulated as quadratically constrained quadratic binary programs. We investigate several semidefinite relaxations by combining known reformulation techniques recently proposed for generic quadratic binary problems with problem-specific strengthening procedures developed for lot-sizing problems. Our computational results show that the semidefinite relaxations consistently provide lower bounds of significantly improved quality as compared with those provided by the best previously published linear relaxations. In particular, the gap between the semidefinite relaxation and the optimal integer solution value can be closed for a significant proportion of the small-size instances, thus avoiding to resort to a tree search procedure. The reported computation times are significant. However improvements in SDP technology can still be expected in the future, making SDP based approaches to discrete lot-sizing more competitive.     

Message passing interface and multithreading hybrid for parallel molecular docking of large databases on petascale high performance computing machines

A mixed parallel scheme that combines message passing interface (MPI) and multithreading was implemented in the AutoDock Vina molecular docking program. The resulting program, named VinaLC, was tested on the petascale high performance computing (HPC) machines at Lawrence Livermore National Laboratory. To exploit the typical cluster‐type supercomputers, thousands of docking calculations were dispatched by the master process to run simultaneously on thousands of slave processes, where each docking calculation takes one slave process on one node, and within the node each docking calculation runs via multithreading on multiple CPU cores and shared memory. Input and output of the program and the data handling within the program were carefully designed to deal with large databases and ultimately achieve HPC on a large number of CPU cores. Parallel performance analysis of the VinaLC program shows that the code scales up to more than 15K CPUs with a very low overhead cost of 3.94%. One million flexible compound docking calculations took only 1.4 h to finish on about 15K CPUs. The docking accuracy of VinaLC has been validated against the DUD data set by the re‐docking of X‐ray ligands and an enrichment study, 64.4% of the top scoring poses have RMSD values under 2.0 Å. The program has been demonstrated to have good enrichment performance on 70% of the targets in the DUD data set. An analysis of the enrichment factors calculated at various percentages of the screening database indicates VinaLC has very good early recovery of actives. © 2013 Wiley Periodicals, Inc.     

Targeting tribbles homolog 3 (TRIB3) protein against type 2 diabetes for the identification of potential inhibitors by in silico screening

Tribbles homolog 3 (TRIB3) protein is inhibiting the insulin signaling by directly binding to the Akt/PKB leading to insulin resistance in the pancreas causing type 2 diabetes mellitus. Hence, TRIB3 protein is considered as a possible drug target for the new lead identification against type 2 diabetes. In the present study, the homology model of TRIB3 protein was generated to explore its biochemical function and molecular interactions in the new lead identification. The energy minimization of TRIB3 protein was carried out and evaluated by validation protocols for structure reliability. The druggable binding site of TRIB3 protein was identified for the virtual screening and molecular docking studies. The Asinex-fragments library of 22634 small molecules was docked at TRIB3 active site using the Glide module to identify new chemical entities. A total of 9 molecules were identified as final hits from virtual screening and their potency was ranked using Glide score, Glide energies, and residues interactions. The 6 prioritized lead molecules were further optimized using AutoDock, Prime MM/GBSA, and percentage of human oral absorption for the identification of potential leads. The molecules L2, L5, and L6 are identified as lead inhibitors and are showing consistent interactions with key residues Glu194 and Lys196 of TRIB3 protein. The identified potential leads were analyzed by ADME properties for their drug likeness and HergIC50 values are predicted for the prevention of preclinical failures. The present work sheds light on the identification of the best lead molecules against TRIB3 protein and offers a route to design as novel potential drug candidates for T2DM.     

In silico virtual screening of potent inhibitor to hamper the interaction between HIV-1 integrase and LEDGF/p75 interaction using E-pharmacophore modeling,molecular docking,and dynamics simulations

The rapid increase of HIV-1 infection throughout the globe has a high demand for a superior drug with lesser side effects. LEDGF/p75, the human Lens Epithelium-Derived Growth Factor is identified as a promising cellular cofactor with integrase in facilitating the viral replication in an early stage by acting as a tethering factor in the pre-integration to the chromatin. Therefore, the present study was designed to identify a potent inhibitor by applying an E-pharmacophore based virtual screening, molecular docking, and dynamics simulation approaches. Finally, ZINC22077550 and ZINC32124441 were best identified potent molecules with the efficient binding affinity, strong hydrogen bonding, and acceptable pharmacological properties to hamper the interaction between integrase and LEDGF/p75. Further, the DFT and MDS studies were also analyzed, and shown a favorable energetic state and dynamic stability then reference compound. In conclusion, we suggest that these findings could be novel therapeutics in the future and may increase the lifespan of individuals suffering from viral infection.