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As the need for quickly exploring a textile manufacturing process is increasingly costly along with the complexity in the process. The development of manufacturing process modeling has attracted growing attention from the textile industry. More and more researchers shift their attention from classic methods to the intelligent techniques for process modeling as the traditional ones can hardly depict the intricate relationships of numerous process factors and performances. In this study, the literature investigating the process modeling of textile manufacturing is systematically reviewed. The structure of this paper is in line with the procedure of textile processes from yarn to fabrics, and then to garments. The analysis and discussion of the previous studies are conducted on different applications in different processes. The factors and performance properties considered in process modeling are collected in comparison. In terms of inputs’ relative importance, feature selection, modeling techniques, data distribution, and performance estimations, the considerations of the previous studies are analyzed and summarized. It is also concluded the limitations, challenges, and future perspectives in this issue on the basis of the summaries of more than 130 related articles from the point of views of textile engineering and artificial intelligence.

The construction industry is crucial for economic and social development. Inorganic materials, which rely on natural minerals and are affected by uncertainties, hold a large share in the construction market. As building materials are from process—intensive industries, complex and continuous processing magnifies deviations, directly affecting product quality. Computational intelligent methods are effective for accurately predicting product quality. This paper focuses on inorganic building materials and systematically reviews computational intelligent techniques in this field. It comprehensively explores 234 related studies in 6 key areas (concrete, ceramics, glass, clay bricks, cement, and steel), compiles prediction models, evaluates them, analyzes model configurations and properties to gain insights into the field and identify optimal approaches. It points out model limitations, such as high computational costs, data-hungry, and suggests future research directions like practicality and promoting green initiatives through material circulation.

Process industry suffers from production management in terms of efficiency promotion and waste reduction in large scale manufacturing due to poor organization of the intricate relational databases. In order to enhance the suitability of intelligent manufacturing systems in process industry, this study proposed an innovative top-down structure Knowledge Graph (KG) for process fault diagnosis, and papermaking was taken as a case study. The KG consists of a normalized seven-step-built ontology, which extracted instances of papermaking knowledge via Protégé software. The exported OWL file was imported into Neo4j software for visualization of the KG. The application in papermaking drying process for fault diagnosis shows that it can depict the material and energy flows throughout the process with a clearer relationship visualization than traditional measures. They also enable rationale search for faults and identification of their potential causes. The built KG efficiently manages the vast knowledge of the process, stores unstructured data, and promotes the intelligent development of process with high reusability and dynamicity that can rapidly import new production knowledge as well as flexibly self-updating.

Epigenetic alteration of tumor suppression gene is one of the most significant indicators in human esophageal squamous cell carcinoma (ESCC). In this study, we identified a novel ESCC hypermethylation biomarker ZNF132 by integrative computational analysis to comprehensive genome-wide DNA methylation microarray dataset. We validated the hypermethylation status of ZNF132 in 91 Chinese Han ESCC patients and adjacent normal tissues with methylation target bisulfite sequencing (MTBS) assay. Meanwhile, ZNF132 gene silencing mediated by hypermethylation was confirmed in both solid tissues and cancer cell lines. What is more, we found that in vitro overexpression of ZNF132 in ESCC cells could significantly reduce the abilities of the cell in growth, migration and invasion, and tumorigenicity of cells in a nude mouse model. We validated the Sp1-binding site in the ZNF132 promoter region with chromatin immunoprecipitation assay and demonstrated that the hypermethylation status could reduce the Sp1 transcript factor activity. Our results suggest that ZNF132 plays an important role in the development of ESCC as a tumor suppressor gene and support the underlying mechanism caused by the DNA hypermethylation-mediated Sp1-binding decay and gene silencing.

Non-small cell lung cancer is one of the most common cancers and the leading cause of cancer death worldwide. Genetic variants in regulatory regions of some miRNAs might be involved in non-small cell lung cancer susceptibility and survival. rs12220909 (G/C) genetic polymorphism in miR-4293 has been shown to be associated with decreased risk of esophageal squamous cell carcinoma. However, the influence of rs12220909 genetic variation on non-small cell lung cancer susceptibility has not been reported. In order to evaluate the potential association between miR-4293 rs12220909 and non-small cell lung cancer risk in a Chinese population, we performed a case-control study among 998 non-small cell lung cancer cases and 1471 controls. The data shows that miR-4293 rs12220909 was significantly associated with decreased susceptibility to non-small cell lung cancer (GC vs.GG: OR = 0.681, 95%CI = 0.555-0.835, P = 2.19E-4; GG vs. GC+CC: OR = 0.687, 95%CI = 0.564-0.837, P = 1.95E-4), which indicates that rs12220909 in miR-4293 may play a significant role in the development of non-small cell lung cancer.

With the development of the customization concept, small-batch and multi-variety production will become one of the major production modes, especially for fast-moving consumer goods. However, this production mode has two issues: high production cost and the long manufacturing period. To address these issues, this study proposes a multi-objective optimization model for the flexible flow-shop to optimize the production scheduling, which would maximize the production efficiency by minimizing the production cost and makespan. The model is designed based on hybrid algorithms, which combine a fast non-dominated genetic algorithm (NSGA-II) and a variable neighborhood search algorithm (VNS). In this model, NSGA-II is the major algorithm to calculate the optimal solutions. VNS is to improve the quality of the solution obtained by NSGA-II. The model is verified by an example of a real-world typical FFS, a tissue papermaking mill. The results show that the scheduling model can reduce production costs by 4.2% and makespan by 6.8% compared with manual scheduling. The hybrid VNS-NSGA-II model also shows better performance than NSGA-II, both in production cost and makespan. Hybrid algorithms are a good solution for multi-objective optimization issues in flexible flow-shop production scheduling.

Sodium hypochlorite bleaching washing process has been broadly carried out in denim garment industrial production. However, the quantitative relationships between process variables and bleaching performances have not been illustrated explicitly. Hence, it is impractical to determine values of the variables that can achieve the optimal production cost while satisfying the requirements of customers. This paper proposes an optimization methodology by combining ensemble of surrogates (ESs) with particle swarm optimization (PSO) to optimize production cost of chlorine bleaching for denim. The methodology starts from the data collections by conducting a Taguchi L25 (56) orthogonal experiment with the process variables and metrics for evaluating bleaching performances. Based on the data, the quantitative relationships are separately constructed by using RBFNN, SVR, RF and ensemble of them. Then, accuracies of the surrogates are evaluated and it proves that the ESs outperforms the others. Later, the production cost optimization model is proposed and PSO is utilized to solve it, while a case study is given to depict the optimization process and verify the effectiveness of the proposed hybrid ESs-PSO approach. Overall, the ESs-PSO approach shows great capability of optimizing production cost of sodium hypochlorite bleaching washing for denim.

Prostate cancer is a major burden on public health and a major cause of morbidity and mortality among men worldwide. Drug combination therapy is known as a powerful tool for the treatment of cancer. The aim of this study is to evaluate the synergistic inhibitory mechanisms of clofoctol and sorafenib in the treatment of prostate cancer. However, the molecular mechanisms of this phenomenon have not been illuminated clearly. In this study, we investigated the anti-tumor effects of clofoctol in combination with sorafenib in vitro and in vivo. The activity and mechanism of clofoctol in combination with sorafenib were examined in PC-3cells. mRNA and protein expression of key players in the ER stress pathway were detected with RT-PCR and Western blotting. Cell viability was estimated by CCK-8 assay or Alamar blue assay, and apoptosis and cell cycle were monitored and measured by flow cytometry. PC-3 cells were inoculated subcutaneously in male BALB/c nude mice. The therapeutic regimen was initiated when the tumor began showing signs of growth and treatment continued for 5 weeks. Our data indicate that clofototol and sorafenib induce cell death through synergistic induction of endoplasmic reticulum (ER) stress, resulting in activation of the unfolded protein response (UPR). Combination therapy with clofoctol and sorafenib induced an upregulation of markers of all three ER stress pathways: PERK, IRE1 and ATF6. In addition, combination therapy with clofoctol and sorafenib markedly inhibited the growth of prostate cancer xenograft tumors, compared with clofoctol or sorafenib alone. The combination of clofoctol and sorafenib can serve as a novel clinical treatment regimen, potentially enhancing antitumor efficacy in prostate cancer and decreasing the dose and adverse effects of either clofoctol or sorafenib alone. These results lay the foundation for subsequent research on this novel therapeutic regimen in human prostate cancer.