Multi-position refining furnaces are a critical strategy for energy-intensive industries to meet its demands of fast-paced production. In most literature, however, they serve only as a buffer, ...holding up to at most two ladles to maintain the proper temperature of ladles. These studies do not take full advantage of them, nor do they study the production scheduling of energy-intensive enterprises with multi-position refining furnaces under time-of-use (TOU) tariffs. Therefore, this paper presents a steelmaking-continuous casting (SCC) scheduling problem with multi-position refining furnaces under TOU tariffs. We firstly develop a mixed integer nonlinear programming (MINLP) model with the goals of minimizing jobs completion time, machines idle time, and total electricity costs, which subjects to the double-position characteristics and other process constraints. Owing to the complexity between the time-slots of TOU tariffs and the processing cycles of jobs, we design an intermediate function to calculate objectives efficiently. Furthermore, a Lagrangian relaxation (LR) algorithm based on a subgradient algorithm is utilized to solve the proposed model, and an interior point algorithm is adopted to solve sub-problems obtained by job-level and batch-level decomposition, whose solution approximates optimality comparing to GUROBI solver. The computational results demonstrate that the solution of job-level decomposition algorithm approximates the optimal scheduling scheme in an acceptable time and is superior to that of GUROBI solver. In addition, double-position instance can find a better scheduling scheme than nondouble-position one.
As one of the demand-side programs, time-of-use (TOU) tariffs brings opportunities of maintaining power grid stability for electricity providers and chances of energy conservation for manufacturers, ...but it also brings challenge for enterprises to optimize scheduling schemes. This paper studies a two-stage parallel machine scheduling problem under TOU to minimize total electricity costs. The two-stage parallel machine system is composed of identical parallel speed-scaling machines at stage 1 and unrelated parallel machines at stage 2. The key issues lie in assigning a group of jobs to a set of parallel machines at each stage and choosing the appropriate processing speed for all jobs at stage 1, and then determining the interval of processing time for jobs on each selected machine. To solve this problem, a new continuous-time mixed-integer linear programming model is formulated. According to the characteristics of this model, a tabu search-greedy insertion hybrid (TS-GIH) algorithm is designed, which realizes job-machine assignment based on load balancing principle, job insertion with greedy mechanism as well as movement and speed adjustment strategies to find more suitable positions for jobs. The effectiveness of the proposed TS-GIH is demonstrated by comparing with CLPEX and improved genetic algorithm (IGA) through real-life and randomly generated instances. The results show that TS-GIH can realize the trade-off between computation time and solution quality. Compared with CLPEX, the computation time of TS-GIH is significantly less, and the solution quality is much better than IGA.
Machinery fault diagnosis tasks have been well addressed when sufficient and abundant data are available. However, the data imbalance problem widely exists in real-world scenarios, which leads to the ...performance deterioration of fault diagnosis markedly. To solve this problem, we present a novel imbalanced fault diagnosis method based on the enhanced generative adversarial networks (GAN). By artificially generating fake samples, the proposed method can mitigate the loss caused by the lack of real fault data. Specifically, in order to improve the quality of generated samples, a new discriminator is designed using spectrum normalization (SN) strategy and a two time-scale update rule (TTUR) method is used to stabilize the training process of GAN. Then, an enhanced Wasserstein GAN with gradient penalty is developed to generate high-quality synthetic samples for the fault samples set. Finally, a deep convolutional classifier is constructed to carry out fault classification. The performance and effectiveness of the proposed method are validated on the Case Western Reserve University bearing dataset and rolling bearing dataset acquired from our laboratory. The simulation results show that the proposed method has a superior performance than other methods for imbalanced fault diagnosis tasks.
Background
Both C-reactive protein (CRP) level and neutrophil-to-lymphocyte ratio (NLR) are commonly elevated in patients with asthma. It is necessary to develop a novel marker, the combined score of ...CRP level and NLR (C-NLR score) based on cutoff points of CRP and NLR, and apply it in asthma diagnosis. The aim of this study was to explore whether C-NLR could distinguish children with exacerbated asthma.
Methods
Children suffering from exacerbated asthma were regarded as the asthmatic group (n = 86), which was divided into three groups: mild (n = 54), moderate (n = 17), and severe (n = 15). The control group consisted of children without any allergic disease and infection (n = 38). To compare CRP level and NLR between the asthmatic group and control group, a receiver-operating characteristic curve was constructed to determine area under the curve (AUC) and optimal cutoff point. Thereafter, the C-NLR score was classified as follows: C-NLR score of 2 with an elevated CRP level and high NLR, a C-NLR score of 1 with one of these abnormalities, and a C-NLR score of 0 with a normal CRP level and low NLR. The C-NLR score was then compared among different asthma groups.
Results
In the control group, the CRP level and NLR were 1.9 (0.5–2.6) mg/L and 1.01 (0.69–1.31), respectively. In the asthmatic group, the CRP level and NLR were 7.3 (3.2–14.2) mg/L and 3.08 (1.73–5.34), respectively, which were higher than those in the control group (p < 0.001 for CRP and p < 0.001 for NLR). The AUC of CRP was 0.86, and the optimal cutoff point was 3.6 mg/L. The AUC of NLR was 0.86, and the optimal cutoff point was 1.72. The AUC of the C-NLR score was 0.94, and the optimal cutoff point was 1.
Conclusions
C-NLR, a novel inflammatory marker, was applied here for the exacerbated asthma for the first time. Our study has shown C-NLR is a promising marker to distinguish children with exacerbated asthma from healthy children.
Triple-negative breast cancer (TNBC) is the subtype with the worst prognosis of breast cancer. Ferroptosis, a novel iron-dependent programmed cell death, has an increasingly important role in ...tumorigenesis and development. However, there is still a lack of research on the relationship between ferroptosis-related genes and the prognosis of TNBC. In this study, we obtained the gene expression profile of TNBC patients and matched clinical data from The Cancer Genome Atlas (TCGA) database. Univariate Cox analysis was used to screen out ferroptosis-related genes associated with TNBC prognosis. Then, the least absolute shrinkage and selection operator (LASSO) regression analysis was employed to establish a prognostic prediction model. A 15-ferroptosis-related gene prognostic prediction model was developed, which classified patients into low-risk (LR) or high-risk (HR) groups. Kaplan-Meier analysis results showed that the prognosis of the LR group was better. The receiver operating characteristic (ROC) curve also confirmed the satisfactory predictive ability of this model. Evaluation of the immune microenvironment of TNBC patients in the HR and LR group suggested these 15 ferroptosis-related genes might affect the prognosis of TNBC by regulating the tumor microenvironment. Our prognostic model can provide a theoretical basis for accurate prognosis prediction of TNBC in clinical practice.
IL-6 is a pleotropic cytokine that acts as a pro-inflammatory mediator and acute-phase response inducer, but has also been reported to possess anti-inflammatory properties. The objective of this ...study was to assess the validity of serum IL-6 test for diagnosis of asthma.
A literature search was conducted using PubMed, Embase, and Cochrane library from January 2007 to March 2021 to identify relevant studies. Eleven studies were included in this analysis, involving 1977 patients with asthma and 1591 healthy non-asthmatic controls. The meta-analysis was performed using Review Manager 5.3 software and Stata 16.0. Random effect model or fixed effect model (FEM) was used to estimate the standardized mean differences (SMDs) with 95% confidence intervals (CIs).
The meta-analysis results revealed that the serum IL-6 levels were higher in asthmatic patients than healthy non-asthmatic controls (SMD 1.31, 95% CI 0.82-1.81, P < 0.00001). IL-6 levels are significantly elevated in pediatric patients with asthma (SMD 1.58, 95% CI 0.75-2.41, P = 0.0002) and mildly elevated in adult patients with asthma (SMD 1.08, 95% CI 0.27-1.90, P = 0.009). In addition, a subgroup analysis of asthma disease status showed that IL-6 levels were increased in stable (SMD 0.69, 95% CI 0.28-1.09, P = 0.009) and exacerbation asthma (SMD 2.15, 95% CI 1.79-2.52, P < 0.00001) patients.
The results of this meta-analysis suggest that serum IL-6 levels were significantly elevated in asthmatic patients as compared to normal population. IL-6 levels can be used as an auxiliary indicator to distinguish individuals with asthma from healthy non-asthmatic controls.
The flexible job shop scheduling problem (FJSSP) and multi-row workshop layout problem (MRWLP) are two major focuses in sustainable manufacturing processes. There is a close interaction between them ...since the FJSSP provides the material handling information to guide the optimization of the MRWLP, and the layout scheme affects the effect of the scheduling scheme by the transportation time of jobs. However, in traditional methods, they are regarded as separate tasks performed sequentially, which ignores the interaction. Therefore, developing effective methods to deal with the multi-objective energy-aware integration of the FJSSP and MRWLP (MEIFM) problem in a sustainable manufacturing system is becoming more and more important. Based on the interaction between FJSSP and MRWLP, the MEIFM problem can be formulated as a multi-objective bi-level programming (MOBLP) model. The upper-level model for FJSSP is employed to minimize the makespan and total energy consumption, while the lower-level model for MRWLP is used to minimize the material handling quantity. Because the MEIFM problem is denoted as a mixed integer non-liner programming model, it is difficult to solve it using traditional methods. Thus, this paper proposes an improved multi-objective hierarchical genetic algorithm (IMHGA) to solve this model. Finally, the effectiveness of the method is verified through comparative experiments.
Collaborative filtering (CF) is one of the most popular approaches to building Recommender systems (RS) and has been extensively implemented in many online applications. But it still suffers from the ...new user cold start problem that users have only a small number of items interaction or purchase records in the system, resulting in poor recommendation performance. Thus, we design a new similarity model which can fully utilize the limited rating information of cold users. We first construct a new metric, Popularity-Mean Squared Difference, considering the influence of popular items, average difference between two user's common ratings and non-numerical information of ratings. Moreover, the second new metric, Singularity-Difference, presents the deviation degree of favor to items between two users. It considers the distribution of the similarity degree of co-ratings between two users as weight to adjust the deviation degree. Finally, we take account of user's personal rating preferences through introducing the mean and variance of user ratings. Experiment results based on three real-life datasets of MovieLens, Epinions and Netflix demonstrate that the proposed model outperforms seven popular similarity methods in terms of MAE, precision, recall and F1-Measure under new user cold start condition.
To improve energy efficiency and maintain the stability of the power grid, time-of-use (TOU) electricity tariffs have been widely used around the world, which bring both opportunities and challenges ...to the energy-efficient scheduling problems. Single machine scheduling problems under TOU electricity tariffs are of great significance both in theory and practice. Although methods based on discrete-time or continuous-time models have been put forward for addressing this problem, they are deficient in solution quality or time complexity, especially when dealing with large-size instances. To address large-scale problems more efficiently, a new greedy insertion heuristic algorithm with a multi-stage filtering mechanism including coarse granularity and fine granularity filtering is developed in this paper. Based on the concentration and diffusion strategy, the algorithm can quickly filter out many impossible positions in the coarse granularity filtering stage, and then, each job can find its optimal position in a relatively large space in the fine granularity filtering stage. To show the effectiveness and computational process of the proposed algorithm, a real case study is provided. Furthermore, two sets of contrast experiments are conducted, aiming to demonstrate the good application of the algorithm. The experiments indicate that the small-size instances can be solved within 0.02 s using our algorithm, and the accuracy is further improved. For the large-size instances, the computation speed of our algorithm is improved greatly compared with the classic greedy insertion heuristic algorithm.
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