Transformation of semantic knowledge into simulation-based decision support

Simulation is capable to cope with the uncertain and dynamic nature of industrial value chains. However, in-depth system expertise is inevitable for mapping objects and constraints from the real world to a virtual model. This knowledge-intensity leads to long development times of respective projects, which contradicts the need for timely decision support. Since more and more companies use industrial knowledge graphs and ontologies to foster their knowledge management, this paper proposes a framework on how to efficiently derive a simulation model from such semantic knowledge bases. As part of the approach, a novel Simulation Ontology provides a standardized meta-model for hybrid simulations. Its instantiation enables the user to come up with a fully parameterized formal simulation model. Newly developed Mapping Rules facilitate this process by providing guidance on how to turn knowledge from existing ontologies, which describe the system to be simulated, into instances of the Simulation Ontology. The framework is completed by a parsing procedure for an automated transformation of this conceptual model into an executable one. This novel modeling approach makes model development more efficient by reducing its complexity. It is validated in a use case implementation from semiconductor manufacturing, where cross-domain knowledge was required in order to model and simulate the impacts of the COVID-19 pandemic on a global supply chain network.     

井下人员定位系统读卡器存储算法分析与测试

从查找时间与存储容量2个方面分析了适用于井下人员定位系统读卡器的3种存储算法,即链式存储算法、哈希表存储算法和哈希二叉树存储算法,给出了该3种不同存储算法的使用效率与应用场合。测试结果表明,链式存储算法查找时间呈线性增长,哈希表存储算法明显优越于链式存储算法,如果测试数据增多,其优越性会更明显;当数据量较大时,哈希搜索二叉树存储算法又明显优于哈希表存储算法,当人数达到行业所要求的8 000人以上时,选择哈希搜索二叉树存储算法更为合理。     

Transparency in complex dynamic food supply chains

Food supply chains are increasingly complex and dynamic due to (i) increasing product proliferation to serve ever diversifying and globalising markets as a form of mass customisation with resulting global flows of raw materials, ingredients and products, and (ii) the need to satisfy changing and variable consumer and governmental demands with respect to food safety, animal welfare, and environmental impact. Transparency in the food supply chain is essential to guarantee food quality and provenance to all users of food and food products. Intensified information exchange and integrated information systems involving all chain actors are needed to achieve transparency with respect to a multitude of food properties.In this paper, specific challenges of food supply chains are highlighted. Major elements are addressed that support transparency to consumers, the government and food companies, which are considered the claimants of transparency. Elements considered to be enablers of transparency are governance mechanisms, quality and safety standards and information exchange. The paper specifies these transparency claimants and enablers for food supply chains and identifies major information system functions and information technology applications needed to comply with transparency demands. It thereby provides a framework for transparency analysis in food supply chains.     

Machine-Learning based methods in short-term load forecasting

Short-term load forecasting is of great significance to the secure and efficient operation of power systems. However, loads can be affected by a variety of external impact factors and thus involve high levels of uncertainties. So it is a challenging task to achieve an accurate load forecast. This paper discusses three commonly-used machine-learning methods used for load forecasting, i.e., the support vector machine method, the random forest regression method, and the long short-term memory neural network method. The features and applications of these methods are analyzed and compared. By integrating the advantages of these methods, a fusion forecasting approach and a data preprocessing technique are proposed for improving the forecasting accuracy. A comparative study based on real load data is performed to verify that the proposed approach is capable of achieving a relatively higher forecasting accuracy.     

等温核酸放大技术在重金属检测方面的应用

等温核酸放大技术是一种可以实现重金属污染物痕量分析的高灵敏性检测方法,在分析检测中有着广泛且重要的作用。本文综述了几种不同的等温核酸放大技术及其在重金属检测方面的应用,阐述分析了各方法的检测原理,为之后建立更加灵敏、快速、准确的检测方法提供了参考。虽然等温核酸放大技术目前主要停留在实验室阶段,并且检测目标有限,但是由于该技术的高灵敏性、特异性以及与其他学科紧密的联系性,具有潜在的应用前景。     

The Importance of Free Fatty Chain Length on the Lipid Organization in the Long Periodicity Phase

The skin’s barrier ability is an essential function for terrestrial survival, which is controlled by intercellular lipids within the stratum corneum (SC) layer. In this barrier, free fatty acids (FFAs) are an important lipid class. As seen in inflammatory skin diseases, when the lipid chain length is reduced, a reduction in the barrier’s performance is observed. In this study, we have investigated the contributing effects of various FFA chain lengths on the lamellar phase, lateral packing. The repeat distance of the lamellar phase increased with FFA chain length (C20–C28), while shorter FFAs (C16 to C18) had the opposite behaviour. While the lateral packing was affected, the orthorhombic to hexagonal to fluid phase transitions were not affected by the FFA chain length. Porcine SC lipid composition mimicking model was then used to investigate the proportional effect of shorter FFA C16, up to 50% content of the total FFA mixture. At this level, no difference in the overall lamellar phases and lateral packing was observed, while a significant increase in the water permeability was detected. Our results demonstrate a FFA C16 threshold that must be exceeded before the structure and barrier function of the long periodicity phase (LPP) is affected. These results are important to understand the lipid behaviour in this unique LPP structure as well as for the understanding, treatment, and development of inflammatory skin conditions.     

Regulatory Light Chains in Cardiac Development and Disease

The role of regulatory light chains (RLCs) in cardiac muscle function has been elucidated progressively over the past decade. The RLCs are among the earliest expressed markers during cardiogenesis and persist through adulthood. Failing hearts have shown reduced RLC phosphorylation levels and that restoring baseline levels of RLC phosphorylation is necessary for generating optimal force of muscle contraction. The signalling mechanisms triggering changes in RLC phosphorylation levels during disease progression remain elusive. Uncovering this information may provide insights for better management of heart failure patients. Given the cardiac chamber-specific expression of RLC isoforms, ventricular RLCs have facilitated the identification of mature ventricular cardiomyocytes, opening up possibilities of regenerative medicine. This review consolidates the standing of RLCs in cardiac development and disease and highlights knowledge gaps and potential therapeutic advancements in targeting RLCs.     

Preparation of High Strength and Toughness Aramid Fiber by Introducing Flexible Asymmetric Monomer to Construct Misplaced-Nunchaku Structure

High performance fibers with high strength and toughness have great potential in composites, but contradiction between tensile strength and elongation at break makes the preparation to become a current challenge. Herein, an asymmetric structure of more flexible diamine, 3,4′-diaminodiphenyl ether (3,4′-ODA), is introduced into heterocyclic aramid (PBIA) fibers to replace rigid symmetric p-phenylenediamine (PDA). By studying the properties of copolymer (mPEBA) fibers with different ratios of diamine, it is found that the mPEBA fiber reached the optimal mechanical properties with the 30% content of 3,4′-ODA. Compared with homopolymerized heterocyclic aramid fibers, the tensile strength and elongation at break of mPEBA fiber are improved by 26.2% and 38.7%, respectively. Results of X-ray diffraction show that the introduction of 3,4′-ODA structure can increase stretchability of mPEBA fibers, improving the orientation degree during hot-drawing. Molecular dynamics simulations confirm that 3,4′-ODA structure undergoes a conformation transformation to form a straightened chain during hot-drawing, while symmetrical 4,4′-diaminodiphenyl ether (4,4′-ODA) cannot form the same conformation. The misplaced-nunchaku structure is formed based on the special meta-para position of 3,4′-ODA, achieving the synergy of high strength and high toughness.     

基于异构能源区块链的综合能源系统交易模型

综合能源系统是对电、气、热、冷能进行统一规划调度的综合系统,顺应了供给侧改革的趋势.目前综合能源系统的用户呈现个体化、分散化的趋势,传统的单向集中式输配电服务模式逐渐不适应这种趋势.基于区块链技术,设计了一种综合能源系统交易模型.采用异构能源区块链的结构模式,利用智能合约建立了基于价格约束的匹配拍卖机制,保障了清洁能源用户的供能优先级,并采用双重认证机制,保障了交易的可靠性.在实验环境下对所设计模型进行分析,仿真结果验证了所提综合能源系统交易模型的有效性和可行性.     

炼油厂价值链分析与优化

运用价值链分析方法对炼油厂内、外部的价值活动进行了分析,将炼油厂内部价值链划分为主生产作业链、辅助生产作业链和辅助价值链三部分,通过分析指出产品结构不合理、炼油成本较高是影响炼油业务价值的主要原因,针对企业实际情况,提出价值链优化调整策略:提高炼油技术水平,优化装置结构和产品结构,增产高附加值产品,不断提高企业经济效益。