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Transition-metal phosphides have been investigated for microwave absorption applications owing to their excellent conduction loss and magnetic property. In this study, novel composites comprised of porous Al 2 O 3 matrix loaded with Fe x Co y P (Fe x Co y P/Al 2 O 3) were prepared by vacuum impregnation, hydrothermal, and low-temperature phosphatizing methods. The experimental results show that Fe x Co y P is uniformly deposited on the surface of porous Al 2 O 3 ceramic, and the Fe x Co y P transforms from needle-like to particle-like structures as the Fe/Co ratio increases from 1:3 to 3:1. The minimum reflection loss (RL min) of FeCo 3 P/Al 2 O 3 composite reaches −26.19 dB at 12.4 GHz with a matching thickness of 2.6 mm, and the maximum effective absorption bandwidth (EAB) achieves 4.2 GHz in the whole X-band at 3.2 mm thicknesses. This study confirmed that the porous Fe x Co y P/Al 2 O 3 composites are potential candidates for microwave absorption applications, and their microwave absorption properties can be enhanced by adjusting the ratio of Fe to Co. [ABSTRACT FROM AUTHOR]

In order to predict the long-term creep life of P91 steel, this work proposed a coupled model, combining the error-trained back-propagation artificial neural network (BP-ANN) and an improved θ model. Both the short-term rupture life obtained by creep experiments and the related creep data of National Institute for Materials Science (NIMS) database were used to validate the above model. In this model, creep parameters (temperature, stress) were used as the input and relative error of 5 θ model extrapolations as the output. Then, the prediction errors of BP-ANN method were introduced to correct the 5 θ model extrapolation results to achieve a more accurate prediction. Consequently, the long-term creep life of P91 steel was predicted. It demonstrated that with the proposed model, the prediction capability for long-term creep life of P91 steel with 200,000 h is much better than those of other θ methods, which has a prediction uncertainty less than 5%. • A model coupled the improved 5 θ model with BP-ANN was proposed. • The proposed model based on error-training predicted the long-term creep life of P91 steel successfully. • The prediction accuracy of proposed model was much better than those of the 4 θ and 5 θ models. [ABSTRACT FROM AUTHOR]

In this paper, we study a single machine scheduling problem by simultaneously considering the processing method of serial-batching, learning effect, resource-dependent processing times, and setup operations. We consider minimizing the makespan as the objective of the studied problem under the constraint that the total resource consumption does not exceed a given limit. For the special case where the resource allocation is given, we first propose the structural properties for job batching policies and batching sequencing, and an optimal batching policy is derived based on these properties. Then, we develop a novel hybrid GSA–TS algorithm which combines the Gravitational Search Algorithm (GSA) and the Tabu Search (TS) algorithm to solve the general case. Computational experiments with different scales show the effectiveness and efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The reliable prediction of long-term creep life is of great importance in order to ensure the service safety of high-temperature components. In this work, the novel Wilshire-Cano-Stewart (WCS) relationships were introduced to model the creep behavior and predict the long-term creep life of P91 steel based on the short-term creep data. The accelerated creep experiments were firstly carried out at the temperature range of 580–620 °C, while the stresses varied from 135 to 200 MPa. According to the analyses of normalized-mean-squared error (NMSE) and mean logarithmic error (MLE), it demonstrated that the WCS model can express all the obtained creep data very well due to the average relative errors lower than 10%. Meanwhile, these values are much lower than those of traditional Time-Temperature-Parameter (TTP) models. In addition, the WCS model can provide the reasonable estimates of 200,000 h creep life with accelerated creep measurements lasting up to only 2000 h. • WCS model made full use of Wilshire and Sinh model respective advantages and eliminated known weaknesses. • Results of WCS model are better than the traditional L-M model in life prediction. • Results of WCS model are accurate enough in life prediction. [ABSTRACT FROM AUTHOR]

Green tide outbreaks caused by overgrowth of Ulva prolifera in the Yellow Sea of China can cause serious ecological stress with concomitant economic hardships, especially to marine fisheries. In this study, short-term effects (14 days) were evaluated using fresh algae U. prolifera (FU), and a 7-day assessment of the effects of decomposing U. prolifera (DU) algal effluent was conducted to determine the effects on the environmental and intestinal microbiota, intestinal transcriptome and mortality of the commercial marine benthic fish, Japanese flounder (Paralichthys olivaceus). The results revealed that algal degradation altered the microbial community structure of fish farm water and fish intestines and increased the relative abundance of the pathogens Flavobacteriaceae in water and Vibrio in fish intestines. Fish intestinal tissue structure appeared to be damaged, as indicated in pathological sections, and transcriptome analysis showed intestinal inflammation after exposure, which may have caused an increase in fish mortality. The degradation of U. prolifera led to a bloom of potential pathogenic bacteria and the inflammation of fish intestines, which resulted in disease in the flounder population that reduced fish harvests and might pose a potential health threat. [Display omitted] • U. prolifera decomposition affected the microbiota in the water and fish intestines. • Potential pathogens Flavobacteriaceae in water significantly increased. • The conditional pathogen Vibrio increased significantly in the intestine of Japanese flounder. • U. prolifera exposure damaged fish intestinal structure and increased mortality. • DEGs related to immunity and inflammation in the intestine changed significantly. [ABSTRACT FROM AUTHOR]

The analysis of aircraft fuel consumption remains an important topic as fuel consumption constitutes a large portion of airline operational costs, and exhaust emissions generated by aircraft gradually have become an essential aspect of urban air pollution, especially during takeoff and descent. Previous studies have focused mainly on optimizing fuel loss and greenhouse gases emission via three key methods: reducing additional aircraft load, using inventive air transfer control methods, and identifying the best-performing aviation systems for all conditions. However, there are few studies that have considered the relationships among fuel consumption, pilot behavior, and flight factors. This study fills this gap by focusing on the relationships between fuel consumption and three factors during the descent phase: altitude, weight, and speed. Further, we propose an improved K-means clustering algorithm to analyze the flight data, conducting clustering analysis on multi-dimensional data based on real flight records of Boeing 737s operated by China Eastern Airlines. We conclude the highlights of this study into three aspects: (1) the relationship between aircraft descent and fuel consumption is investigated through clustering analysis to find a fuel-efficient aircraft descent. (2) an improved K-means clustering algorithm is proposed to analyze flight data. (3) the better aircraft descent is the suggestion for pilots by analyzing the clustering results, which is a good supplement to the Flight Crew Operating Manual (FCOM). In the case study, the improved K-means algorithm is used for the cluster analysis of data from the quick access recorders of two aircraft descending into Chengdu Shuangliu Airport (CTU) in China. Compared with the method that does not use the analysis results for the descending, the average fuel consumption per unit of time decreases by 17.5% when we use our proposed method. In the Shanghai Pudong Airport (PVG), our proposed method reduces average fuel consumption per unit of time by 19.3%. [ABSTRACT FROM AUTHOR]

This paper investigates an order acceptance and scheduling problem with an energy consumption budget, a machine launch budget, and the order release time in a green manufacturing system. The phenomenon of deteriorating jobs is considered in the production of the accepted orders. The objective of the study is to maximize the net revenue and a modified variable neighborhood search (MVNS) Algorithm that combines a novel encoding and decoding procedure as well as a dynamic programming algorithm is developed to solve it. To show the effectiveness and efficiency of the proposed algorithm, we first employ the MVNS algorithm and seven VNS-based algorithms to solve the problems with different configurations of orders and machines. The results show that the MVNS algorithm obtains better solutions than existing VNS-based algorithms. Then, the proposed algorithm is compared with three meta-heuristic algorithms. The experimental results show that the proposed algorithm has significant advantages in terms of solution optimality. [ABSTRACT FROM AUTHOR]