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This paper proposes a nonlinear model predictive control (NMPC) strategy based on a local model network (LMN) and a heuristic optimization method to solve the control problem for a nonlinear boiler–turbine unit. First, the LMN model of the boiler–turbine unit is identified by using a data-driven modeling method and converted into a time-varying global predictor. Then, the nonlinear constrained optimization problem for the predictive control is solved online by a specially designed immune genetic algorithm (IGA), which calculates the optimal control law at each sampling instant. By introducing an adaptive terminal cost in the objective function and utilizing local fictitious controllers to improve the initial population of IGA, the proposed NMPC can guarantee the system stability while the computational complexity is reduced since a shorter prediction horizon can be adopted. The effectiveness of the proposed NMPC is validated by simulations on a 500 MW coal-fired boiler–turbine unit.

At present, there are many studies on \"computer immunology\" in the world, and there are many applications of immune genetic algorithm in engineering.The article discusses the immune genetic algorithm in the design of PID intelligent controller.Most objects in the practical industrial process fall in the distributed parameter system (DPS), where the proportional-integral-derivative (PID) control method is used to control the field. In this paper, the PID controller optimization method based on the immune genetic algorithm (IGA) (referred to as the PODM method) is applied to a class of distributed parameter objects to design the optimal controller, which is compared with several controllers based on the conventional tuning formulas. The simulation results suggest that the PID controller designed based on the PODM method can obtain relatively superior control effects in overshoot, tuning time, time integrated time absolute error (ITAE), and other indexes with relatively less control energy. The application of the PODM method to DPS can improve the control level of the PID controller in the industry at present.

Regression testing of evolving software is a critical constituent of the software development process. Due to resources constraints, test case prioritization is one of the strategies followed in regression testing during which a test case that satisfies predefined objectives the most, as the tester perceives, would be executed the earliest. In this study, all the experiments were performed on three web applications consisting of 65 to 100 pages with lines of code ranging from 5000 to 7000. Various state-of-the-art approaches such as, heuristic approaches, Greedy approaches, and meta heuristic approaches were applied so as to identify the prioritized test sequence which maximizes the value of average percentage of fault detection. Performance of these algorithms was compared using different parameters and it was concluded that the Artificial Bee Colony algorithm performs better than all. Two novel greedy algorithms are also proposed in the study, of which the goal is to smartly manage the state of a tie, where a tie exhibits the condition that all the test cases participating in the tie are of equal significance in achieving the objective. It has also been validated that the performance of these novel proposed algorithm(s) is better than that of traditionally followed greedy approach, most of the time.

Dynamic deformation data analysis and prediction is a complex systematic project. Aimed at the shortcoming of the traditional prediction model, a method to design the BP neural network based on Immune Genetic Algorithm(IGA) was proposed. The mechanisms of diversity maintaining and antibody density regulation exhibited in a biological immune system were introduced into IGA based on genetic algorithm. The proposed algorithm overcame the problems of GA on search efficiency, individual diversity and prematur, and enhanced the convergent performance effectively. The results show that the BP neural network designed by IGA have better performance in convergent speed and global convergence, and the forecasting accuracy is improved, which illustrates IGA-BP neural network has certain of value on dynamic deformation monitoring forecasting.

This paper proposed a new optimization algorithm-immune genetic algorithm (IGA), which is based on immune genetic theory of creatures and can simulate the immune system and its behaviour in organisms. Compared with common genetic algorithms, the IGA adopts the following techniques to improve the global searching ability and convergence speed: immune memory, immune selection, concentration control, niching technique, chaos production and metabolism. Several typical test functions are used to verify the excellent performances of the proposed IGA. Finally, the IGA is applied to optimize the parameters of a unified power flow controller (UPFC) to improve the stability of the New England Test Power System (NETPS). Numerical simulation results demonstrate the validity of the optimized UPFC controller.

Background IgA nephropathy (IgAN) is one of the most common primary glomerulonephritis, which is a significant cause of renal failure. At present, the classification of IgAN is often limited to pathology, and its molecular mechanism has not been established. Therefore we aim to identify subtypes of IgAN at the molecular level and explore the heterogeneity of subtypes in terms of immune cell infiltration, functional level. Methods Two microarray datasets (GSE116626 and GSE115857) were downloaded from GEO. Differential expression genes (DEGs) for IgAN were screened with limma. Three unsupervised clustering algorithms (hclust, PAM, and ConsensusClusterPlus) were combined to develop a single-sample subtype random forest classifier (SSRC). Functional subtypes of IgAN were defined based on functional analysis and current IgAN findings. Then the correlation between IgAN subtypes and clinical features such as eGFR and proteinuria was evaluated by using Pearson method. Subsequently, subtype heterogeneity was verified by subtype-specific modules identification based on weighted gene co-expression network analysis(WGCNA) and immune cell infiltration analysis based on CIBERSORT algorithm. Results We identified 102 DEGs as marker genes for IgAN and three functional subtypes namely: viral-hormonal, bacterial-immune and mixed type. We screened seventeen genes specific to viral hormonal type (ATF3, JUN and FOS etc.), and seven genes specific to bacterial immune type (LIF, C19orf51 and SLPI etc.). The subtype-specific genes showed significantly high correlation with proteinuria and eGFR. The WGCNA modules were in keeping with functions of the IgAN subtypes where the MEcyan module was specific to the viral-hormonal type and the MElightgreen module was specific to the bacterial-immune type. The results of immune cell infiltration revealed subtype-specific cell heterogeneity which included significant differences in T follicular helper cells, resting NK cells between viral-hormone type and control group; significant differences in eosinophils, monocytes, macrophages, mast cells and other cells between bacterial-immune type and control. Conclusion In this study, we identified three functional subtypes of IgAN for the first time and specific expressed genes for each subtype. Then we constructed a subtype classifier and classify IgAN patients into specific subtypes, which may be benefit for the precise treatment of IgAN patients in future.

Background Immunoglobulin A nephropathy (IgAN) is one of the most common primary glomerulonephritis and a serious health concern worldwide; though still the underlying molecular mechanisms of IgAN are yet to be known and there is no efficient treatment for this disease. The main goal of this study was to explore the IgAN underlying pathogenic pathways, plus identifying the disease correlated modules and genes using the weighted gene co-expression network analysis (WGCNA) algorithm. Results GSE104948 dataset (the expression data from glomerular tissue of IgAN patients) was analyzed and the identified differentially expressed genes (DEGs) were introduced to the WGCNA algorithm for building co-expression modules. Genes were classified into six co-expression modules. Genes of the disease’s most correlated module were mainly enriched in the immune system, cell–cell communication and transmembrane cell signaling pathways. The PPI network was constructed by genes in all the modules and after hub-gene identification and validation steps, 11 genes, mostly transmembrane proteins ( CD44 , TLR1 , TLR2 , GNG11 , CSF1R , TYROBP , ITGB2 , PECAM1 ), as well as DNMT1 , CYBB and PSMB9 were identified as potentially key players in the pathogenesis of IgAN. In the constructed regulatory network, hsa-miR-129-2-3p , hsa-miR-34a-5p and hsa-miR-27a-3p , as well as STAT3 were spotted as top molecules orchestrating the regulation of the hub genes. Conclusions The excavated hub genes from the hearts of co-expressed modules and the PPI network were mostly transmembrane signaling molecules. These genes and their upstream regulators could deepen our understanding of IgAN and be considered as potential targets for hindering its progression.