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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]

In this paper, we study a scheduling model in which the features of deteriorating jobs, serial batches, multiple job types, and setup times are considered simultaneously. In this proposed model, the jobs of each type are first partitioned into serial batches, and then all batches of different job types are processed on a single serial-batching machine. The actual job processing time is an increasing function of its starting time, and the setup time of the batches is sequence-dependent, i.e., setup time is required only when a new batch is processed first on the machine or immediately after a batch belonging to another job type. We develop optimization algorithms to solve the makespan minimization problem, the maximum tardiness minimization problem, the maximum lateness minimization problem, and the maximum earliness minimization problem, respectively. We also propose optimization algorithms to solve the problem of minimizing the number of tardy jobs under a certain agreeable condition. Finally, we discuss two special cases of the total completion time minimization problem and develop optimization algorithms to solve them. [ABSTRACT FROM AUTHOR]

Many dynamic events exist in real manufacturing systems, such as arbitrary machine breakdowns and dynamic job arrivals, which makes the scheduling problem even more complicated. In this paper, we address a serial-batching scheduling problem with the above dynamic events. Jobs need to be processed on the serial-batching machines of two manufacturers and then transported by vehicles to a customer for further processing. The objective of the scheduling problem is to minimize the makespan, and the problem is proved to be strongly NP-hard. Some structural properties and a lower bound of the problem are also proved or derived. On the basis of job arrival times, we divide the problem into two phases and propose different rules regarding these two phases. Based on these properties and rules, a heuristic algorithm is developed to solve the problem and its worst case performance is analyzed. The heuristic algorithm is tested on a large set of randomly generated problem instances, and the relative gaps between the found lower bound and the solutions of the proposed heuristic algorithm are reported. The experimental results illustrate the high efficiency and effectiveness of the proposed heuristic algorithm compared with other four classic approaches. [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]

Recovery of the end-of-use products has become a topic of considerable interest in the advanced manufacturing industry due in part to uncertainties in the quality and volume of product returns. The Internet of Things (IoT) that enables the tracing, detecting, storing, and analyzing the product life cycle data for each individual item can mitigate or eliminate these uncertainties. In this paper, an integrated three-stage model is presented based on IoT technology for the optimization of procurement, production and product recovery, pricing and strategy of return acquisition. The remaining value is used to measure the return condition. The model considers three recovery options related to refurbishing, component reuse and disposal, and the value deterioration for satisfying the product demand in each stage of product life cycle (PLC). A novel particle swarm optimization (PSO) algorithm based on two heuristic methods is proposed to solve the problem. A numerical example and sensitivity analysis are used to illustrate the performance of both algorithm and applicability of the model. [ABSTRACT FROM AUTHOR]

This paper investigates a products and vehicles scheduling problem in a two-stage supply chain environment, where jobs first need to be processed on the serial batching machines of multiple manufacturers distributed in various geographic zones and then transported by vehicles to a customer for further processing. The size and processing time of jobs are varying with the difference of types, and each batch takes a setup time before being processed. The problem of minimizing the makespan is formalized as a mixed integer programming model and proved to be NP-hard. In addition, the structural properties and lower bound of the problem are analyzed and inferred. Then a modified gravitational search algorithm (MGSA) is proposed to solve the problem. In the developed MGSA, several improvement strategies and the batching mechanism DP-H are introduced. The effectiveness and efficiency of the proposed MGSA are demonstrated and compared with a particle swarm optimization (PSO) algorithm and a genetic algorithm (GA). Besides, the error ratios between the lower bound and the best found solutions are reported. The experimental results indicate that the proposed MGSA is more robust and outperforms PSO and GA on the studied two-stage supply chain scheduling problem. [ABSTRACT FROM AUTHOR]

The ultrasonic attenuation of ductile cast irons with different matrices was investigated by means of ultrasonic echo waves. In the ductile cast irons with ferritic and pearlitic matrix structures, both of the ultrasonic attenuation increased with frequency. For similar frequencies, the ultrasonic attenuation of the pearlitic matrix was larger than that of the ferritic matrix. Based on the theory of ultrasonic attenuation in the solid, the mechanisms of ultrasonic attenuation in the ductile cast irons with different matrices were analyzed. It indicated that in the ductile cast irons with transformation of matrix from the ferrite to the pearlite, the mechanism of ultrasonic attenuation varied in the range of present frequencies. In the ferritic matrix, the total ultrasonic attenuation was mainly attributed to the scattering loss which included the stochastic scattering and the Rayleigh scattering. On the contrary, the absorption loss predominated in the total ultrasonic attenuation of the ductile cast iron with pearlitic matrix. [ABSTRACT FROM AUTHOR]

Presents a letter to the editor about damping behavior of cast iron with aluminum addition.

This article presents information on the effect of aluminum addition on the damping capacity of cast iron. Meanwhile, in order to analyze the damping mechanism, the microstructure of cast iron was also investigated. High purity iron, carbon, silicon and aluminum were used to prepare samples. The melting process was carried out in an induction-melting furnace and then the melts were cast into the sand mold. Damping capacity of sample was measured by the free-decaying oscillation method. Recently, several studies have focused on the development of ecocirculation materials by recycling the used steel drink cans in foundries, which have both high heat resistance and good wear resistance.

The Tsiolkovsky crater is located on the farside of the Moon. It formed in the late Imbrian epoch and was filled with a large area of mare basalts. Multisource remote sensing data are used to interpret the geological features of the Tsiolkovsky area. Compared with previous studies, new remote sensing data and a chronological model based on crater size–frequency distribution are used to further refine the stratigraphic units and determine the absolute ages of the mare basalt units. The evolution of volcanic activity in this crater is discussed. The results are as follows: Abundances of major elements, Th, and silicate minerals suggest that the mare basalt in the crater floor is not a uniform unit but rather nine units with different compositions. The nine basalt units are divided into two episodes of volcanic activity: The first occurred at 3.5–3.7 Ga, when highly evolved lava erupted at the crater floor at a large scale; the second occurred at ~3.4 Ga, when a small area of more primitive lava extended to the northern portion of the crater floor. [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 introduces the serial batching scheduling problems with position-based learning effect, where the actual job processing time is a function of its position. Two scheduling problems respectively for single-machine and parallel-machine are studied, and in each problem the objectives of minimizing maximum earliness and total number of tardy jobs are both considered respectively. In the proposed scheduling models, all jobs are first partitioned into serial batches, and then all batches are processed on the serial-batching machine. We take some practical production features into consideration, i.e., setup time before processing each batch increases with the time, regarded as time-dependent setup time, and we formalize it as a linear function of its starting time. Under the single-machine scheduling setting, structural properties are derived for the problems with the objectives of minimizing maximum earliness and number of tardy jobs respectively, based on which optimization algorithms are developed to solve them. Under the parallel-machine scheduling setting, a hybrid VNS-GSA algorithm combining variable neighborhood search (VNS) and gravitational search algorithm (GSA) is proposed to solve the problems with these two objectives respectively, and the effectiveness and efficiency of the proposed VNS-GSA are demonstrated and compared with the algorithms of GSA, VNS, and simulated annealing (SA). This paper demonstrates that the consideration of different objectives leads to various optimal decisions on jobs assignment, jobs batching, and batches sequencing, which generates a new insight to investigate batching scheduling problems with learning effect under single-machine and parallel-machine settings. [ABSTRACT FROM AUTHOR]

Maintenance cost of aircraft engine is an important component of aircraft operation cost. The prediction of aircraft engine maintenance cost can provide decision support for airline to make reasonable maintenance plan and maintenance fund management. Considering that the prediction accuracy of engine maintenance cost is not high in the case of small samples, this paper proposes a bootstrapped support vector regression (SVR) prediction method based on improved recursive feature elimination, which realizes the point and interval prediction of engine maintenance cost in aircraft operation phase. First, the recursive feature elimination (RFE) is improved and then combined with SVR to select feature subsets. Second, particle swarm optimization (PSO) algorithm is applied to optimize the improved RFE-SVR model (IRFE-SVR) parameters. Finally, the point and interval estimates are obtained by bootstrapped IRFE-SVR. To demonstrate the performance of the bootstrapped IRFE-SVR, experiments on UCI and a real case study of engine maintenance cost prediction are conducted. The results on UCI and real datasets show that the bootstrapped IRFE-SVR method has high accuracy and reliability. [ABSTRACT FROM AUTHOR]

This paper investigates coordinated scheduling on uniform parallel batch machines with batch transportation. Jobs are characterized by different processing time and sizes, and they are first delivered to manufacturers in batches and then processed on the uniform parallel batch machines. The manufacturers are distributed in different geographic zones and there exists one parallel batch machine in each manufacturer. A mixed integer programming model is developed for the studied problem, and its objective is to minimize the makespan. In addition, the structural properties of the problem are analyzed. A hybrid algorithm combining the merits of discrete particle swarm optimization (DPSO) and genetic algorithm (GA) is proposed to solve this problem. In the hybrid algorithm, a heuristic and a local search strategy are introduced. Finally, computational experiments are conducted and the results show that the proposed hybrid algorithm can effectively and efficiently solve the problem within a reasonable time, particularly in large-scale instances. [ABSTRACT FROM AUTHOR]

Understanding hot carrier dynamics between plasmonic nanomaterials and its adsorbate is of great importance for plasmon‐enhanced photoelectronic processes such as photocatalysis, optical sensing and spectroscopic analysis. However, it is often challenging to identify specific dominant mechanisms for a given process because of the complex pathways and ultrafast interactive dynamics of the photoelectrons. Here, using CO2 reduction as an example, the underlying mechanisms of plasmon‐driven catalysis at the single‐molecule level using time‐dependent density functional theory calculations is clearly probed. The CO2 molecule adsorbed on two typical nanoclusters, Ag20 and Ag147, is photoreduced by optically excited plasmon, accompanied by the excitation of asymmetric stretching and bending modes of CO2. A nonlinear relationship has been identified between laser intensity and reaction rate, demonstrating a synergic interplay and transition from indirect hot‐electron transfer to direct charge transfer, enacted by strong localized surface plasmons. These findings offer new insights for CO2 photoreduction and for the design of effective pathways toward highly efficient plasmon‐mediated photocatalysis. [ABSTRACT FROM AUTHOR]

This paper addresses a serial-batching scheduling problem where the jobs with arbitrary release times are scheduled on parallel machines with the objective to minimize the makespan. The effects of learning and deterioration are considered simultaneously, and each job's actual processing time depends on the sum of previous jobs' processing times and the position of the current job. Each machine can process up to c jobs in the manner of serial batch, i.e., one after another with a setup time for each batch. Structural properties are identified for the special cases of the studied problem. Based on these derived structural properties, we propose a novel hybrid SC-VNS algorithm to solve the studied problem, which combines Society and Civilization (SC) algorithm with Variable Neighborhood Search (VNS). Computational experiments are conducted to evaluate the performance of the proposed hybrid algorithm and some other well-known algorithms. The results demonstrate that the proposed hybrid SC-VNS algorithm performs quite better than the compared algorithms in terms of the solution quality and the required running time. [ABSTRACT FROM AUTHOR]

This paper investigates a competitive two-agent parallel-batching scheduling problem with aging effect on parallel machines. The objective is to minimize the makespan of agent A with the constraint that the makespan of agent B is no more than a given threshold. Some key structural properties are first identified in two different cases, and based on these structural properties a novel decision tree of scheduling rules is constructed and a heuristic algorithm is designed. Then, an effective hybrid BF-VNS algorithm combining Bacterial Foraging (BF) with variable neighborhood search (VNS) is developed to tackle the studied problem. Computational experiments are conducted to evaluate the performance of the proposed hybrid algorithm and some other well-known algorithms. The experimental results indicate that the hybrid BF-VNS algorithm performs quite better than the compared algorithms. [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]

Abstract: New product development (NPD) is crucial for a company’s success in a competitive market. Meanwhile, NPD is a process associated with great complexity and high risk. To ensure its smooth operation, risks involved in an NPD process need to be analyzed in a proper way. In this paper, a novel method is proposed to generate a belief rule base (BRB), which is the basis of the Belief Rule-Base Inference Methodology using the Evidential Reasoning (RIMER). Due to its capability in dealing with complex reasoning problems under uncertainty, RIMER is then applied to assess customer perception risk (CPR) in an NPD process. To test and validate the method proposed in this paper, a case study of an “Interactive Doll” is conducted at the end of the paper. [ABSTRACT FROM AUTHOR]