Hybrid Petri-nets for modelling and performance evaluation of supply chains

Modelling and analysis of complex and co-ordinated supply chains is a crucial task due to its inherent complexity and uncertainty. Therefore, the current research direction is to devise an efficient modelling technique that maps the dynamics of a real life supply chain and assists industrial practitioners in evaluating and comparing their network with other competing networks. Here an effective modelling technique, the hybrid Petri-net, is proposed to efficiently handle the dynamic behaviour of the supply chain. This modelling methodology embeds two enticing features, i.e. cost and batch sizes, in deterministic and stochastic Petri-net for the modelling and performance evaluation of supply chain networks. The model is subsequently used for risk management to investigate the issues of supply chain vulnerability and risk that has become a major research subject in recent years. In the test bed, a simple productive supply chain and an industrial supply chain are modelled with fundamental inventory replenishment policy. Subsequently, its performance is evaluated along with the identification and assessment of risk factors using analytical and simulation techniques respectively. Thus, this paper presents a complete package for industrial practitioners to model, evaluate performance and manage risky events in a supply chain.     

Roles of the Unsaturated Fatty Acid Docosahexaenoic Acid in the Central Nervous System: Molecular and Cellular Insights

Fatty acids (FAs) are essential components of the central nervous system (CNS), where they exert multiple roles in health and disease. Among the FAs, docosahexaenoic acid (DHA) has been widely recognized as a key molecule for neuronal function and cell signaling. Despite its relevance, the molecular pathways underlying the beneficial effects of DHA on the cells of the CNS are still unclear. Here, we summarize and discuss the molecular mechanisms underlying the actions of DHA in neural cells with a special focus on processes of survival, morphological development, and synaptic maturation. In addition, we examine the evidence supporting a potential therapeutic role of DHA against CNS tumor diseases and tumorigenesis. The current results suggest that DHA exerts its actions on neural cells mainly through the modulation of signaling cascades involving the activation of diverse types of receptors. In addition, we found evidence connecting brain DHA and ω-3 PUFA levels with CNS diseases, such as depression, autism spectrum disorders, obesity, and neurodegenerative diseases. In the context of cancer, the existing data have shown that DHA exerts positive actions as a coadjuvant in antitumoral therapy. Although many questions in the field remain only partially resolved, we hope that future research may soon define specific pathways and receptor systems involved in the beneficial effects of DHA in cells of the CNS, opening new avenues for innovative therapeutic strategies for CNS diseases.     

关于细胞神经网络无性模板的设计

本文以连通片检测器无性模板的设计为例，给出了细胞神经网络中无性模板的设计方法及连通片检测器无性模板中各元素的数值范围。     

人工水晶的压痕断裂行为及其 K_(1c)测量

藉助于声发射技术,研究了在 Vickers 钻石压头作用下人工水晶的压痕断裂行为。压头对角线与晶体试样的相对取向是,使得水晶Ⅰ和水晶Ⅱ的压痕诱发径向/中位断裂,分别沿着(10(?)0)/(1(?)10)和(11(?)0/(1(?)00)晶面对发展。压痕断裂过程中的声发射特征表明,除了随着压痕特征参数的发展所引起的准静态—声发射外,有三个相互独立的,与径向/中位裂纹的成核、径向/中位裂纹突入试样表面、以及裂纹分岔或侧向裂纹萌生等过程相关的声发射效应。以 Vickers 压痕技术测得 K_(c2)的结果与以一般力学法及 Knoop 压痕技术的测试结果作了比较。断裂韧性与晶体学取向的关系亦作了分析。实验表明,水晶Ⅰ和水晶Ⅱ具有相同的 K_(1c)值。其原因是,两种试样的径向/中位裂纹实际上沿着同样的{1100}/{1120}晶面对扩展。至于从缺口梁法或 Knoop 压痕技术得到的 K_(1c)值,由于水晶Ⅰ和水晶Ⅱ的主裂纹分别沿着(1(?)10)和(1(?)00)晶面扩展;它们的 K_(1c)值与断裂能随着晶体学取向而变有关,因而也就各不相同。     

采用磁流变阻尼器的汽车悬架系统的杂交建模

利用线性回归方法,对磁流变阻尼器阻尼力与速度、加速度间的函数关系作了线性与非线性的区分,采用神经网络对非线性部分进行建模,并结合线性部分构建了磁流变阻尼器的模型。在此基础上,融合汽车悬架的先验知识模型,建立了采用磁流变阻尼器的4自由度1/2车辆半主动悬架系统的杂交模型。最后利用SANTANA 2000型轿车的参数进行仿真,并与基于磁流变阻尼器非线性滞回模型的建模方法作了比较,结果表明:杂交建模方式结构简单、计算量小、模型准确、便于进行系统主要动力学特性的分析。     

Towards 3D Bioprinted Spinal Cord Organoids

Three-dimensional (3D) cultures, so-called organoids, have emerged as an attractive tool for disease modeling and therapeutic innovations. Here, we aim to determine if boundary cap neural crest stem cells (BC) can survive and differentiate in gelatin-based 3D bioprinted bioink scaffolds in order to establish an enabling technology for the fabrication of spinal cord organoids on a chip. BC previously demonstrated the ability to support survival and differentiation of co-implanted or co-cultured cells and supported motor neuron survival in excitotoxically challenged spinal cord slice cultures. We tested different combinations of bioink and cross-linked material, analyzed the survival of BC on the surface and inside the scaffolds, and then tested if human iPSC-derived neural cells (motor neuron precursors and astrocytes) can be printed with the same protocol, which was developed for BC. We showed that this protocol is applicable for human cells. Neural differentiation was more prominent in the peripheral compared to central parts of the printed construct, presumably because of easier access to differentiation-promoting factors in the medium. These findings show that the gelatin-based and enzymatically cross-linked hydrogel is a suitable bioink for building a multicellular, bioprinted spinal cord organoid, but that further measures are still required to achieve uniform neural differentiation.     

Classification and prediction of burr formation in micro drilling of ductile metals

In the micro drilling of precision miniature holes, the formation of exit burrs is a topic of interest, especially for ductile materials. Because such burrs are difficult to remove, it is important to be able to predict various burr types and to employ burr minimisation schemes that consider burrs’ micro-scale characteristics. In the present work, an artificial neural network (ANN) was used to predict the formation of burrs in the micro drilling of copper and brass, along with burr formation/optimisation analysis specialised for micro drills. The influence of cutting conditions, including cutting speed, feed and drill diameter, upon exit micro burr characteristics such as burr size and type was observed, analysed and classified. Based on the results, an empirical equation to predict micro burr height is proposed herein. The classification results were compared with conventional burr cases using burr control charts. Then, micro burr types were predicted by means of an ANN, using the influential parameters as input vectors. The usefulness of the proposed scheme was demonstrated by comparing the experimental and prediction/analysis results.     

Research on prediction for coal and gas outburst based on Matlab neural network toolbox and its application

In order to predict the danger of coal and gas outburst in mine coal layer correctly, on the basis of the VLBP and LMBP algorithm in Matlab neural network toolbox, one kind of modified BP neural network was put forth to speed up the network convergence speed in this paper. Firstly, according to the characteristics of coal and gas outburst, five key influencing factors such as excavation depth, pressure of gas, and geologic destroy degree were selected as the judging indexes of coal and gas outburst. Secondly, the prediction model for coal and gas outburst was built. Finally, it was verified by practical examples. Practical application demonstrates that, on the one hand, the modified BP prediction model based on the Matlab neural network toolbox can overcome the disadvantages of constringency and, on the other hand, it has fast convergence speed and good prediction accuracy. The analysis and computing results show that the computing speed by LMBP algorithm is faster than by VLBP algorithm but needs more memory. And the resuits show that the prediction results are identical with actual results and this model is a very efficient prediction method for mine coal and gas outburst, and has an important practical meaning for the mine production safety. So we conclude that it can be used to predict coal and gas outburst precisely in actual engineering.     

神经网络智能诊断技术在混凝土结构中的理论实现与展望

混凝土结构诊断技术是现代建筑工业中的一个重要课题。基于人工神经网络的智能诊断,其融合了计算机技术与神经网络技术,并结合了工程设备的特征,它具有非线性、快速、精确等优点,更接近工程实际;同时,具有美好的发展前景。     

ENet-6mA: Identification of 6mA Modification Sites in Plant Genomes Using ElasticNet and Neural Networks

N6-methyladenine (6mA) has been recognized as a key epigenetic alteration that affects a variety of biological activities. Precise prediction of 6mA modification sites is essential for understanding the logical consistency of biological activity. There are various experimental methods for identifying 6mA modification sites, but in silico prediction has emerged as a potential option due to the very high cost and labor-intensive nature of experimental procedures. Taking this into consideration, developing an efficient and accurate model for identifying N6-methyladenine is one of the top objectives in the field of bioinformatics. Therefore, we have created an in silico model for the classification of 6mA modifications in plant genomes. ENet-6mA uses three encoding methods, including one-hot, nucleotide chemical properties (NCP), and electron–ion interaction potential (EIIP), which are concatenated and fed as input to ElasticNet for feature reduction, and then the optimized features are given directly to the neural network to get classified. We used a benchmark dataset of rice for five-fold cross-validation testing and three other datasets from plant genomes for cross-species testing purposes. The results show that the model can predict the N6-methyladenine sites very well, even cross-species. Additionally, we separated the datasets into different ratios and calculated the performance using the area under the precision–recall curve (AUPRC), achieving 0.81, 0.79, and 0.50 with 1:10 (positive:negative) samples for F. vesca, R. chinensis, and A. thaliana, respectively.