An institution theory of formal meta-modelling in graphically extended BNF

Meta-modelling plays an important role in model driven software development.In this paper,a graphic extension of BNF (GEBNF) is proposed to define the abstract syntax of graphic modelling languages.Fro...     

A novel data-driven approach to support decision-making during production scale-up of assembly systems

In today's manufacturing settings, a sudden increase in the customer demand may enforce manufacturers to alter their manufacturing systems either by adding new resources or changing the layout within a restricted time frame. Without an appropriate strategy to handle this transition to higher volume, manufacturers risk losing their market competitiveness. The subjective experience-based ad-hoc procedures existing in the industrial domain are insufficient to support the transition to a higher volume, thereby necessitating a new approach where the scale-up can be realised in a timely, systematic manner. This research study aims to fulfill this gap by proposing a novel Data-Driven Scale-up Model, known as DDSM, that builds upon kinematic and Discrete-Event Simulation (DES) models. These models are further enhanced by historical production data and knowledge representation techniques. The DDSM approach identifies the near-optimal production system configurations that meet the new customer demand using an iterative design process across two distinct levels, namely the workstation and system levels. At the workstation level, a set of potential workstation configurations are identified by utilising the knowledge mapping between product, process, resource and resource attribute domains. Workstation design data of selected configurations are streamlined into a common data model that is accessed at the system level where DES software and a multi-objective Genetic Algorithm (GA) are used to support decision-making activities by identifying potential system configurations that provide optimum scale-up Key Performance Indicators (KPIs). For the optimisation study, two conflicting objectives: scale-up cost and production throughput are considered. The approach is employed in a battery module assembly pilot line that requires structural modifications to meet the surge in the demand of electric vehicle powertrains. The pilot line is located at the Warwick Manufacturing Group, University of Warwick, where the production data is captured to initiate and validate the workstation models. Conclusively, it is ascertained by experts that the approach is found useful to support the selection of suitable system configuration and design with significant savings in time, cost and effort.     

Prediction of venturi scrubber efficiency used in an updraft gasifier cleaning train

The performance of a venturi scrubber in the removal of tar from gas in updraft gasification has been studied. The gasifier has been operated with a husk feed rate of 1.6 × 10^?4 kg/s. The venturi scrubber has been operated at a superficial gas velocity of 56.4 m/s at the throat. A wide variety of scrubbing liquids having surface tensions ranging from 0.026 to 0.072 N/m have been used. The Q_g/Q_l, has been varied in the range of 1000–8000. The tar separation efficiency η has been found to vary from 51 to 98.5%. A mathematical model, assuming steady-state operation, has been developed considering very high pseudosolubility of tar in the scrubbing liquids. The predicted values of η have been compared with experimental results. The model satisfactorily explains the tar removal efficiency of the venturi for Q_g/Q_l values ranging from 4000 to 8000 for all scrubbing liquids. The following correlation has been developed for predicting venturi scrubber efficiency: .     

单相NPC型H桥级联逆变器电容电压不均与环流相互作用机理及建模研究

针对单相NPC型H桥级联逆变器并联时环流与电容电压不均的相互关系进行研究。通过环流等效电路推导了一个开关周期内环流与电容电压不均之间的数学关系,发现电容电压不均差异是环流的激励源之一;同时环流会改善一台并联逆变器的电容电压不均,恶化另一台逆变器的电容电压不均。为建立周期稳态条件下的环流-电容电压不均数学模型,提出采用“假设—建模—求解—检验—校正”的建模思路建立了基于阻滞的环流作用模型,有效避免了多因素建模的复杂性,同时提高了模型的准确性,仿真和试验结果表明,基于阻滞的环流作用模型拟合决定系数可达0.963 8和0.945 0,证明了环流模型的准确性。     

A Holistic Evolutionary and 3D Pharmacophore Modelling Study Provides Insights into the Metabolism,Function, and Substrate Selectivity of the Human Monocarboxylate Transporter 4 (hMCT4)

Monocarboxylate transporters (MCTs) are of great research interest for their role in cancer cell metabolism and their potential ability to transport pharmacologically relevant compounds across the membrane. Each member of the MCT family could potentially provide novel therapeutic approaches to various diseases. The major differences among MCTs are related to each of their specific metabolic roles, their relative substrate and inhibitor affinities, the regulation of their expression, their intracellular localization, and their tissue distribution. MCT4 is the main mediator for the efflux of L-lactate produced in the cell. Thus, MCT4 maintains the glycolytic phenotype of the cancer cell by supplying the molecular resources for tumor cell proliferation and promotes the acidification of the extracellular microenvironment from the co-transport of protons. A promising therapeutic strategy in anti-cancer drug design is the selective inhibition of MCT4 for the glycolytic suppression of solid tumors. A small number of studies indicate molecules for dual inhibition of MCT1 and MCT4; however, no selective inhibitor with high-affinity for MCT4 has been identified. In this study, we attempt to approach the structural characteristics of MCT4 through an in silico pipeline for molecular modelling and pharmacophore elucidation towards the identification of specific inhibitors as a novel anti-cancer strategy.     

Novel Cell Permeable Polymers of N-Substituted L-2,3-Diaminopropionic Acid (DAPEGs) and Cellular Consequences of Their Interactions with Nucleic Acids

The present study aimed to synthesize novel polycationic polymers composed of N-substituted L-2,3-diaminopropionic acid residues (DAPEGs) and investigate their cell permeability, cytotoxicity, and DNA-binding ability. The most efficient cell membrane-penetrating compounds (O2Oc-Dap(GO2)_n-O2Oc-NH₂, where n = 4, 6, and 8) showed dsDNA binding with a binding constant in the micromolar range (0.3, 3.4, and 0.19 µM, respectively) and were not cytotoxic to HB2 and MDA-MB-231 cells. Selected compounds used in the transfection of a GFP plasmid showed high transfection efficacy and minimal cytotoxicity. Their interaction with plasmid DNA and the increasing length of the main chain of tested compounds strongly influenced the organization and shape of the flower-like nanostructures formed, which were unique for 5/6-FAM-O2Oc-[Dap(GO2)]₈-O2Oc-NH₂ and typical for large proteins.     

冷轧带钢板形测控技术的发展状况和关键问题

 冷轧带钢属于高端精品钢材,板形在线检测与控制是冷轧带钢的高端核心关键技术。自主创新研制板形测控系统是实现中国钢铁工业发展升级、建设钢铁强国的重大需求。目前,板形测控技术市场国外占据优势,国产系统正在代替进口,扩大应用规模,推进技术完善。研制先进的板形测控系统需要解决的关键技术有高精度高质量的板形仪、功能完备强大的控制手段和方法、高精度高速度的数学模型。板形仪主要有接触式和非接触式两大类,接触式板形仪通过测量带钢张力的横向分布反映板形,非接触式板形仪通过测量带钢浪形反映板形。接触式板形仪可靠耐用精度高,应用广泛,发展趋势为整辊式板形检测辊、无线式信号传输装置、板形数据的精确处理。板形控制数学模型的主要类型,按建模的原理和方法可分为机理模型和智能模型;按模型的性质和作用可分为分析模型和控制模型;按板形的表示方法可分为多点控制模型和分量控制模型。板形控制模型的发展趋势为机理与智能协同建模、动态模拟预报和动态解耦控制、多种手段和方法的协同优化。进一步提高板形测控技术水平需要突破3项关键问题,即整辊式板形仪通道耦合与解耦的机理模型、板形控制的动态模拟和动态解耦模型、板形控制的高精度智能建模方法。