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This study proposes a novel modified sign coherence factor (SCF) weighting adapted to the frequency-domain (FD) beamforming for ultrasound plane-wave imaging to achieve a high frame rate and better image quality. First, before beamforming, the sign components were extracted from the radiofrequency signals of aperture data. Second, the modified SCF was established using the FD beamformed sign components. Finally, the FD beamformed image was weighted by the modified SCF. To assess the performance of the proposed modified SCF for FD beamforming, the resolution, contrast, computation complexity and execution time of the generated images were evaluated. The results revealed that the FD-SCF could significantly improve the computational load compared with the classic delay-and-sum SCF on the premise of equal image quality improvement. Therefore, high image quality and low computational load have been successfully combined under the proposed weighting method., (Copyright © 2022 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.)

In this work, a circular statistics vector (CSV) weighting Fourier domain (FD) beamforming for ultrasound plane-wave images was proposed to achieve better image quality with a high frame rate. Firstly, the cosine and sine components of the instantaneous phase are extracted from undelayed RF signals. Secondly, the FD beamformed cosine and sine components are used to establish the CSV. Finally, the FD beamformed amplitude image is weighted by the CSV. The resolution, contrast, and computation complexity were used to assess the performance of the proposed method. The results revealed that FD_CSV could significantly reduce the computational load compared to the conventional DAS_CSV on the equal improvement of image quality. Besides, compared to coherence factor (CF), phase coherence factor (PCF), etc., based on variance calculation, the CSV based on mean resultant vector calculation can effectively preserve the speckle due to the more tolerant to phase errors. The proposed FD_CSV weighting method has successfully conducted high image quality and low computational load., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Simple Summary: This study introduces a CT-based 3D reconstruction algorithm for measuring resection margins post sublobar resection in lung cancer patients. It aims to enhance accuracy and reproducibility, addressing the limitations of gross margin measurement. The findings show feasibility with a target registration error of <2.5 mm. This approach could revolutionize lung cancer treatment, facilitating prompt identification of patients with inadequate resection margins for timely post-operative adjuvant therapies. Sublobar resection has emerged as a standard treatment option for early-stage peripheral non-small cell lung cancer. Achieving an adequate resection margin is crucial to prevent local tumor recurrence. However, gross measurement of the resection margin may lack accuracy due to the elasticity of lung tissue and interobserver variability. Therefore, this study aimed to develop an objective measurement method, the CT-based 3D reconstruction algorithm, to quantify the resection margin following sublobar resection in lung cancer patients through pre- and post-operative CT image comparison. An automated subvascular matching technique was first developed to ensure accuracy and reproducibility in the matching process. Following the extraction of matched feature points, another key technique involves calculating the displacement field within the image. This is particularly important for mapping discontinuous deformation fields around the surgical resection area. A transformation based on thin-plate spline is used for medical image registration. Upon completing the final step of image registration, the distance at the resection margin was measured. After developing the CT-based 3D reconstruction algorithm, we included 12 cases for resection margin distance measurement, comprising 4 right middle lobectomies, 6 segmentectomies, and 2 wedge resections. The outcomes obtained with our method revealed that the target registration error for all cases was less than 2.5 mm. Our method demonstrated the feasibility of measuring the resection margin following sublobar resection in lung cancer patients through pre- and post-operative CT image comparison. Further validation with a multicenter, large cohort, and analysis of clinical outcome correlation is necessary in future studies. [ABSTRACT FROM AUTHOR]

In this paper, we deal with the unrestricted block relocation problem. We present a new integer programming formulation for solving the problem. The initial formulation is improved by tightening constraints and a pre-processing step to fix several variables. We design a exact iterative scheme algorithm based on a fast heuristic for the integer programming formulation (ISA-FH). Computational results show the effectiveness of the improved formulation and algorithm. [ABSTRACT FROM AUTHOR]

With the development of synchrophasor measurement techniques, ambient signal-based load modeling is recently proposed to track the time-varying characteristics of power loads. On this basis, the hardware realization of this approach, which includes the local load modeling devices and the data center, is proposed and tested with the field measurement data from China Southern Power Grid. In this way, practical load model parameters can be identified from field measurement data both online and offline by the hardware system. To begin with, the hardware system, which is the first realization of an ambient signal-based load modeling algorithm, offers sufficient field measurements for load modeling. Then, the identification results of the field measurements verify the effectiveness of the ambient signal-based load modeling algorithm. Besides, the identified load model parameters for different substations during different times demonstrate the time-varying and the distributed characteristics of power loads. [ABSTRACT FROM AUTHOR]

Most studies on distributed assembly permutation flowshop scheduling do not consider product priorities and factory heterogeneity. This causes delays in critical products and cannot reflect the real-world production situation. This paper focuses on the energy-efficient heterogeneous distributed assembly permutation flowshop scheduling considering priorities (EHDAPFS-P) to minimize total tardiness and total energy consumption simultaneously. Unlike traditional models, factory heterogeneity and product priorities are considered to better reflect the production environment and customer satisfaction in real-world situations. Then, a Q-learning memetic algorithm (QLMA) is proposed to solve this problem: (i) a high-quality initial population is obtained using a hybrid initialization strategy that combines four problem-specific heuristics; (ii) six efficient neighborhood structures are tailored to guide the population to converge to the promising areas; (iii) the most useful neighborhood structure is selected among the six structures using the Q-learning algorithm to accelerate the convergence, thus maximizing the cumulative and future improvements according to the population state; and (iv) an energy-saving strategy is developed to optimize the total energy consumption without deteriorating the total tardiness. The proposed QLMA is compared with seven state-of-the-art algorithms on 261 benchmark instances to demonstrate its superiority or at least competitiveness. • Factory heterogeneity and product priorities are considered in DAPFSP. • Four problem-specific heuristics are devised to generate the initial population. • Six efficient neighborhood structures are designed. • The best neighborhood structure is determined using the Q-learning algorithm. • An effective energy-saving strategy is developed to reduce TEC. [ABSTRACT FROM AUTHOR]

The work has developed an innovative speed loop controller for the permanent magnet synchronous motor (PMSM) system. The main advantage of the presented method lies in the fact that the boundary of the lumped disturbance is not required. Under this case, the severe chattering caused by the inappropriate choice of parameters can be avoided. Finally, the simulation and experimental consequences are given to demonstrate the efficiency of the proposed algorithm used to control a PMSM system subject to the lumped disturbance. [ABSTRACT FROM AUTHOR]

Considering the complexity of wireless sensor network (WSN) coverage problems, which include many variables and a large continuous search space, a WSN coverage optimization method based on an improved artificial bee colony (ABC) algorithm with teaching strategy is proposed in this paper. ABC, which is good at exploration but poor at exploitation, is improved by introducing a teaching strategy in teaching-learning-based optimization (TLBO) that has a rapid convergence but is easily trapped in a local optima. Thus, the proposed algorithm combines the advantages of ABC strong global search ability and TLBO rapid convergence. In addition, to retain the diversity and eliminate the parameter limit in ABC, a dynamic search update strategy is introduced instead of the scout bee phase of ABC. In addition to preliminary examinations with a number of benchmark functions, the performance of the algorithm is verified by solving a complicated wireless sensor network coverage problem. The simulation results verify that the proposed algorithm achieves better balance between global and local search compared with other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

• Addressing the reconfigurability and grouped families in the DFSP. • Proposing a MILP model of the addressed problem. • Proposing dynamic solution encoding and decoding strategies. • Integrating a nested search strategy into the VND. • Good performance of the proposed approaches on two sets of instances. Inspired by a real-world cellular manufacturing system for processing printed circuit boards (PCBs), this study addresses a reconfigurable distributed flowshop scheduling problem (RDFGSP), where the flowline is considered as a cell and a complete process includes two flows through the flowlines. The characteristics of the RDFGSP lie in the reconfigurability of the flowlines and the families with grouped jobs. To solve this problem, at the two flows the family assignment part and sequence part (including family sequence and job sequence) are all required to be addressed. To solve the problem, a mixed integer linear programming (MILP) model is first developed, which can solve the small-scaled instances to optimality. Since the NP-hard property of the problem, a nested variable neighborhood descent (NVND) algorithm is developed. Its core ideas lie in the dynamic solution encoding and decoding strategies and the specially designed nested loops, including the external and internal loops. In the external loop, the solution encoding and decoding only consider the sequence part, and the family assignment part is conceived using the machine property in the decoding process. The internal loop is triggered when a decoding process in the external loop is completed, where the family assignment part and sequence part are all considered. In addition, a collaborative process and a restart strategy are employed to guarantee the global search capability. In the experimental study, the iterated F-Race (I/F-Race) is used to help determine the algorithm parameters with minimum user intervention. Comprehensive computational results demonstrate that the proposed algorithm outperforms the math solver CPLEX and other state-of-the-art metaheuristics. [ABSTRACT FROM AUTHOR]

Multi-objective evolutionary algorithms (MOEAs) have become an important choice for solving multi-objective optimization problems. The performance of MOEAs is highly dependent on the algorithm configuration. Therefore, the algorithm configuration is an essential task in the development and application of MOEAs. In this paper, a real-time data-driven automatic design method for configuring an MOEA with minimal user interference is developed. Real-time data are driven in two ways. One lies in that the learning model is constructed based on the elite configurations selected by the Iterated F-Race (I/F-Race), which is used to bias the sampling toward the best configurations. Another is that the decision tree model is constructed by collecting the evaluated configurations in the process of I/F-Race as the data, and used to help identify the potential configurations to improve the sampling quality. In addition, a configurable MOEA (CMOEA) framework is summarized by integrating three general fitness assignment methods. In the experimental study, a case study on a multi-objective hybrid flowshop scheduling problem is conducted. By comparing with other variants of I/F-Race, the developed method is verified to have the ability of evaluating the promising configurations more fully and conceiving the best MOEA. Compared with the famous frameworks and state-of-the-art MOEAs, the proposed CMOEA framework and the automated algorithm show their superiorities based on different performance metrics. • A CART-enhanced I/F-Race is developed. • A configurable MOEA framework is developed. • A case study on a multi-objective HFSP is studied. [ABSTRACT FROM AUTHOR]