Towards A Computational Intelligence Framework in Steel Product Quality and Cost Control

Steel is a fundamental raw material for all industries. It can be widely used in vari-ous fields, including construction, bridges, ships, containers, medical devices and cars. However, the production process of iron and steel is very perplexing, which consists of four processes: ironmaking, steelmaking, continuous casting and rolling. It is also extremely complicated to control the quality of steel during the full manufacturing pro-cess. Therefore, the quality control of steel is considered as a huge challenge for the whole steel industry. This thesis studies the quality control, taking the case of Nanjing Iron and Steel Group, and then provides new approaches for quality analysis, manage-ment and control of the industry. At present, Nanjing Iron and Steel Group has established a quality management and control system, which oversees many systems involved in the steel manufacturing. It poses a high statistical requirement for business professionals, resulting in a limited use of the system. A lot of data of quality has been collected in each system. At present, all systems mainly pay attention to the processing and analysis of the data after the manufacturing process, and the quality problems of the products are mainly tested by sampling-experimental method. This method cannot detect product quality or predict in advance the hidden quality issues in a timely manner. In the quality control system, the responsibilities and functions of different information systems involved are intricate. Each information system is merely responsible for storing the data of its corresponding functions. Hence, the data in each information system is relatively isolated, forming a data island. The iron and steel production process belongs to the process industry. The data in multiple information systems can be combined to analyze and predict the quality of products in depth and provide an early warning alert. Therefore, it is necessary to introduce new product quality control methods in the steel industry. With the waves of industry 4.0 and intelligent manufacturing, intelligent technology has also been in-troduced in the field of quality control to improve the competitiveness of the iron and steel enterprises in the industry. Applying intelligent technology can generate accurate quality analysis and optimal prediction results based on the data distributed in the fac-tory and determine the online adjustment of the production process. This not only gives rise to the product quality control, but is also beneficial to in the reduction of product costs. Inspired from this, this paper provide in-depth discussion in three chapters: (1) For scrap steel to be used as raw material, how to use artificial intelligence algorithms to evaluate its quality grade is studied in chapter 3; (2) the probability that the longi-tudinal crack occurs on the surface of continuous casting slab is studied in chapter 4;(3) The prediction of mechanical properties of finished steel plate in chapter 5. All these 3 chapters will serve as the technical support of quality control in iron and steel production

Effects of the probiotic Bacillus amyloliquefaciens on the growth, immunity, and disease resistance of Haliotis discus hannai

Abstract(#br)The effects of a diet containing the probiotic Bacillus amyloliquefaciens on the survival and growth of Haliotis discus hannai were evaluated by measuring growth and hematological parameters and the expression levels of nonspecific immune genes. In addition, the abalone’s response to Vibrio parahaemolyticus infection was assessed. H. discus hannai (shell length: 29.35 ± 1.81 mm, body weight: 4.28 ± 0.23 g) were exposed to an 8-week culture experiment in indoor aquariums and a 2-week V. parahaemolyticus artificial infection experiment. In each experiment, the control group (C) was fed daily with the basal feed; the experimental groups were fed daily with the experimental feed, prepared by spraying B. amyloliquefaciens onto the basal feed at final concentrations of 10 3 (group A1), 10 5 (A2), and 10 7 (A3) cfu/g. The survival rate, body weight specific growth rate, and food conversion efficiency in A2 and A3 were significantly higher than those in A1 and C ( P < 0.05). The total number of blood lymphocytes, the O 2 − and NO levels produced from respiratory burst, the activities of acid phosphatase, superoxide dismutase, and catalase, and the expression levels of catalase and thiol peroxidase in A2 were not significantly different from those in A3, but these factors were significantly higher in A2 compared to A1 and C ( P < 0.05). The total antioxidant capacity and expression levels of glutathione S-transferase in A1, A2 and A3 were significantly higher than those in C ( P < 0.05). At day 9 after infection with V. parahaemolyticus , all abalone in C were dead; at the end of the experiment, the cumulative mortality of abalone in A2 was significantly lower than that in any other group ( P < 0.05). Thus, the experimental feed containing 10 5 cfu/g B. amyloliquefaciens not only facilitated the food intake and growth of abalone, but also effectively enhanced their non-specific immunity and resistance to V. parahaemolyticus infection. In this regard, B. amyloliquefaciens may be a useful probiotic strain for abalone aquaculture

Genome-wide and single-base resolution DNA methylomes of the Pacific oyster <i>Crassostrea gigas</i> provide insight into the evolution of invertebrate CpG methylation

BACKGROUND: Studies of DNA methylomes in a wide range of eukaryotes have revealed both conserved and divergent characteristics of DNA methylation among phylogenetic groups. However, data on invertebrates particularly molluscs are limited, which hinders our understanding of the evolution of DNA methylation in metazoa. The sequencing of the Pacific oyster Crassostrea gigas genome provides an opportunity for genome-wide profiling of DNA methylation in this model mollusc. RESULTS: Homologous searches against the C. gigas genome identified functional orthologs for key genes involved in DNA methylation: DNMT1, DNMT2, DNMT3, MBD2/3 and UHRF1. Whole-genome bisulfite sequencing (BS-seq) of the oyster’s mantle tissues revealed that more than 99% methylation modification was restricted to cytosines in CpG context and methylated CpGs accumulated in the bodies of genes that were moderately expressed. Young repeat elements were another major targets of CpG methylation in oysters. Comparison with other invertebrate methylomes suggested that the 5’-end bias of gene body methylation and the negative correlation between gene body methylation and gene length were the derived features probably limited to the insect lineage. Interestingly, phylostratigraphic analysis showed that CpG methylation preferentially targeted genes originating in the common ancestor of eukaryotes rather than the oldest genes originating in the common ancestor of cellular organisms. CONCLUSIONS: Comparative analysis of the oyster DNA methylomes and that of other animal species revealed that the characteristics of DNA methylation were generally conserved during invertebrate evolution, while some unique features were derived in the insect lineage. The preference of methylation modification on genes originating in the eukaryotic ancestor rather than the oldest genes is unexpected, probably implying that the emergence of methylation regulation in these 'relatively young’ genes was critical for the origin and radiation of eukaryotes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2164-15-1119) contains supplementary material, which is available to authorized users

p97/VCP is highly expressed in the stem-like cells of breast cancer and controls cancer stemness partly through the unfolded protein response

p97/VCP, an evolutionarily concerned ATPase, partakes in multiple cellular proteostatic processes, including the endoplasmic reticulum (ER)-associated protein degradation (ERAD). Elevated expression of p97 is common in many cancers and is often associated with poor survival. Here we report that the levels of p97 positively correlated with the histological grade, tumor size, and lymph node metastasis in breast cancers. We further examined p97 expression in the stem-like cancer cells or cancer stem cells (CSCs), a cell population that purportedly underscores cancer initiation, therapeutic resistance, and recurrence. We found that p97 was consistently at a higher level in the CD4

Intra-Articular Injection of Fructus Ligustri Lucidi Extract Attenuates Pain Behavior and Cartilage Degeneration in Mono-Iodoacetate Induced Osteoarthritic Rats

Fructus Ligustri Lucidi (FLL) has been widely used as a traditional Chinese medicine (TCM) for treating soreness and weakness of waist and knees. It has potential for treating OA owing to its kidney-tonifying activity with bone-strengthening effects, but there is so far no report of its anti-OA effect. This study established a rat OA model by intra-articular (IA) injection of mono-iodoacetate (1.5 mg) and weekly treated by IA administration of FLL at 100 μg/mL for 4 weeks. Thermal withdrawal latency, mechanical withdrawal threshold, and spontaneous activity were tested for evaluation of pain behavior, and histopathological (HE, SO, and ABH staining) and immunohistochemical (Col2, Col10, and MMP13) analyses were conducted for observation of cartilage degradation. In vitro effect of FLL on chondrocytes was evaluated by MTT assay and qPCR analysis. Moreover, HPLC analysis was performed to determine its chemoprofile. The pain behavioral data showed that FLL attenuated joint pain hypersensitivity by increasing thresholds of mechanical allodynia and thermal hyperalgesia as well as spontaneous activity. The histopathological result showed that FLL reversed OA cartilage degradation by protecting chondrocytes and extracellular matrix in cartilage, and the immunohistochemical analysis revealed its molecular actions on protein expressions of MMP13, Col2, and Col10 in cartilage. The MTT assay showed its proliferative effects on chondrocytes, and qPCR assay clarified its mechanism associated with gene expressions of Mmp13, Col2, Col10, Adamts5, Aggrecan, and Runx2 in TNF-α treated chondrocytes. Our results revealed an anti-OA effect of FLL on pain behavior and cartilage degradation in OA rats and clarified a molecular mechanism in association with the suppression of chondrocyte hypertrophy and catabolism. IA FLL can be regarded as novel and promising option for OA therapy

The oyster genome reveals stress adaptation and complexity of shell formation

The Pacific oyster Crassostrea gigas belongs to one of the most species-rich but genomically poorly explored phyla, the Mollusca. Here we report the sequencing and assembly of the oyster genome using short reads and a fosmid-pooling strategy, along with transcriptomes of development and stress response and the proteome of the shell. The oyster genome is highly polymorphic and rich in repetitive sequences, with some transposable elements still actively shaping variation. Transcriptome studies reveal an extensive set of genes responding to environmental stress. The expansion of genes coding for heat shock protein 70 and inhibitors of apoptosis is probably central to the oyster's adaptation to sessile life in the highly stressful intertidal zone. Our analyses also show that shell formation in molluscs is more complex than currently understood and involves extensive participation of cells and their exosomes. The oyster genome sequence fills a void in our understanding of the Lophotrochozoa. © 2012 Macmillan Publishers Limited. All rights reserved

Derivative-Based Acceleration of General Vector Machine

General vector machine (GVM) is one of supervised learning machine, which is based on three-layer neural network. It is capable of constructing a learning model with limited amount of data. Generally, it employs Monte Carlo algorithm (MC) to adjust weights of the underlying network. However, GVM is time-consuming at training and is not efficient when compared with other learning algorithm based on gradient descent learning. In this paper, we present a derivative-based Monte Carlo algorithm (DMC) to accelerate the training of GVM. Our experimental results indicate that DMC algorithm is faster than the original MC method. Specifically, the training time of our DMC algorithm in GVM for function fitting is also less than some gradient descent-based methods, in which we compare DMC with back-propagation neural network. Experimental results indicate that our algorithm is promising for training GVM

A novel Monte Carlo-based neural network model for electricity load forecasting

The ongoing rapid growth of electricity over the past few decades greatly promotes the necessity of accurate electricity load forecasting. However, despite a great number of studies, electricity load forecasting is still an enormous challenge for its complexity. Recently, the developments of machine learning technologies in different research areas have demonstrated its great advantages. General Vector Machine (GVM) is a new machine learning model, which has been proven very effective in time series prediction. In this article, we firstly review the basic concepts and implementation of GVM. Then we apply it in electricity load forecasting, which is based on the electricity load dataset of Queensland, Australia. A detailed comparison with traditional back-propagation neural network (BP) is presented in this paper. To improve the load forecasting accuracy, we specially propose to use the weights-fixed method, ReLu activation function, an efficient algorithm for reducing time and the influence of parameter matrix β to train the GVM model. Analysis of our approach on the historical Queensland electricity load dataset has demonstrated that GVM could achieve better forecasting results, which shows the strong potential of GVM for general electricity load forecasting

A Blockchain based System for Safe Vaccine Supply and Supervision

Immunization is an indispensable mechanism for preventing infectious diseases in modern society, and vaccine safety is closely related to public health and national security. However, issues such as vaccine expiration and vaccine record fraud are still widespread in vaccine supply chains. Therefore, an effective management system for the supervision of vaccine supply chains is urgently required. As the next generation of core technology after the Internet, blockchain is designed to build trust mechanisms that can change current information management methods. Meanwhile, the development of machine learning technologies provides additional ways to analyze the data in information management systems. The main objective of this study is to develop a “vaccine blockchain” system based on blockchain and machine learning technologies. This vaccine blockchain system is designed to support vaccine traceability and smart contract functions, and can be used to address the problems of vaccine expiration and vaccine record fraud. Additionally, the use of machine learning models can provide valuable recommendations to immunization practitioners and recipients, allowing them to choose better immunization methods and vaccines

Ensemble Machine Learning Systems for the Estimation of Steel Quality Control

Recent advances in the steel industry have encountered challenges in soliciting decision making solutions for quality control of products based on data mining techniques. In this paper, we present a steel quality control prediction system encompassing with real-world data as well as comprehensive data analysis results. The core process is cautiously designed as a regression problem, which is then best handled by grouping various learning algorithms with their massive resource of historical production datasets. The characteristics of the currently most popular learning models used in regression problem analysis are as well investigated and compared. The performance indicates our steel quality control prediction system based on ensemble machine learning model can offer promising result whilst delivering high usability for local manufacturers to address the production problem by aid of development of machine learning techniques. Furthermore, real-world deployment of this system is demonstrated and discussed. Finally, future directions and the performance expectation are pointed out