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Simple Summary: Cervidae represents a substantial family within the Artiodactyla, yet their phylogenetic relationship has not been fully clarified due to limited data available. With the increasing availability of genomic data, there is now an opportunity to further explore their phylogenetic relationship. In our study, we employed a comparative genomics approach focusing on the chromosome-level genomes of eight Cervidae species to explore their phylogenetic relationship and chromosomal evolutionary events. Furthermore, we conducted an analysis of gene families to identify those potentially involved in adaptive evolution. Cervidae represents a family that is not only rich in species diversity but also exhibits a wide range of karyotypes. The controversies regarding the phylogeny and classification of Cervidae still persist. The flourishing development of the genomic era has made it possible to address these issues at the genomic level. Here, the genomes of nine species were used to explore the phylogeny and chromosomal evolutionary events of Cervidae. By conducting whole-genome comparisons, we identified single-copy orthologous genes across the nine species and constructed a phylogenetic tree based on the single-copy orthologous genes sequences, providing new insights into the phylogeny of Cervidae, particularly the phylogenetic relationship among sika deer, red deer, wapiti and Tarim red deer. Gene family analysis revealed contractions in the olfactory receptor gene family and expansions in the histone gene family across eight Cervidae species. Furthermore, synteny analysis was used to explore the chromosomal evolutionary events of Cervidae species, revealing six chromosomal fissions during the evolutionary process from Bovidae to Cervidae. Notably, specific chromosomal fusion events were found in four species of Cervus, and a unique chromosomal fusion event was identified in Muntiacus reevesi. Our study further completed the phylogenetic relationship within the Cervidae and demonstrated the feasibility of inferring species phylogeny at the whole-genome level. Additionally, our findings on gene family evolution and the chromosomal evolutionary events in eight Cervidae species lay a foundation for comprehensive research of the evolution of Cervidae. [ABSTRACT FROM AUTHOR]

This study addresses the quay crane scheduling problem (QCSP), which involves scheduling a fixed number of quay cranes to load and unload containers from ships in a maritime container terminal. The objective is to minimize the completion time while adhering to precedence, safety margin, and noncrossing constraints. Efficient scheduling of quay cranes plays a crucial role in reducing the time vessels spend at terminals. To solve the QCSP, we explore different schedule directions for the quay cranes. Specifically, we consider three directions: unidirectional, where the quay cranes maintain a consistent movement direction from upper to lower bays or vice versa after initial repositioning; bidirectional, allowing the cranes to change direction once during operations; and multidirectional, permitting freely changing movement direction during operations. For the bidirectional QCSP, we propose a new compact mathematical formulation. To obtain valid lower bounds on the optimal completion time, we derive various relaxations of this new formulation based on the different schedule directions. Our solution framework employs logic-based Benders decomposition, decomposing the problem into an assignment master problem and operation-sequence slave subproblems. Extensive computational experiments using benchmark instances from existing literature and newly generated instances validate the efficiency and effectiveness of the lower bounds and the exact solution approach. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Funding: This work was supported by the Major Program of the National Natural Science Foundation of China [Grants 72192830 and 72192831], the National Natural Science Foundation of China [Grant 72102034], and the 111 Project [Grant B16009]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2022.0298 [ABSTRACT FROM AUTHOR]

Simple Summary: Livestock and poultry germplasm resources are national strategic resources related to the global revitalization of the seed industry. As an important part of livestock, velvet deer germplasm resources must be taken seriously. Our review focuses on the situation and utilization of velvet deer germplasm resources in China. We mainly introduce the situation of velvet deer germplasm resources in China, such as ecological distribution and the number of subspecies. We further elaborate on the domestication and breeding development of velvet deer in China and put forward suggestions on the scientific utilization of velvet deer germplasm resources. Velvet deer are not only a representative special economic animal but also an important part of livestock. With the increasing awareness of international competition for germplasm resources in China, more and more attention has been paid to the protection and utilization of germplasm resources. However, there is poor understanding about velvet deer resources. Therefore, we are providing a comprehensive introduction of Chinese velvet deer germplasm resources from the aspects of ecological distribution, domestication and breeding. [ABSTRACT FROM AUTHOR]

The task assignment problem (TAP) is concerned with assigning a set of tasks to a set of agents subject to the limited processing and memory capacities of each agent. The objective to be minimized is the total assignment cost and total communication cost. TAP is a relevant model for many practical applications, yet solving the problem is computationally challenging. Most of the current metaheuristic algorithms for TAP adopt population-based search frameworks, whose search behaviors are usually difficult to analyze and understand due to their complex features. In this work, unlike previous population-based solution methods, we concentrate on a single trajectory stochastic local search model to solve TAP. Especially, we consider TAP from the perspective of a grouping problem and introduce the first probability learning-based local search algorithm for the problem. The proposed algorithm relies on a dual probability learning procedure to discover promising search regions and a gain-based neighborhood search procedure to intensively exploit a given search region. We perform extensive computational experiments on a set of 180 benchmark instances with the proposed algorithm and the general mixed integer programming solver CPLEX. We assess the composing ingredients of the proposed algorithm to shed light on their impacts on the performance of the algorithm. Note to Practitioners—This work is motivated by the problem of program modules designing and task allocation in parallel and distribution systems. It can also be applied to deal with job (task) grouping problems in practical industrial applications. This article presents a novel and effective learning-based local search algorithm to obtain high-quality solutions for the considered problem. The results of numerical experiments and comparisons show that our algorithm can achieve good search performances on problem instances of different scales and difficulties. Afterward, we use the proposed solution method to solve a real-life open-order slab assignment problem, which is derived from the production planning of silicon steel for an iron and steel company. The learning techniques of the proposed algorithm are of general interest and can be used in search algorithms for solving other real-life optimization problems with grouping features. For future research, we will design solution methods based on these learning techniques to address other practical optimization problems. [ABSTRACT FROM AUTHOR]

Estimation of distribution algorithm (EDA) is an efficient population-based stochastic search technique. Since it was proposed, many attempts have been made to improve its performance in the context of nonlinear continuous optimization. However, the success of EDA depends on the accuracy of modeling, the effectiveness of sampling, and the ability of exploration. An effective EDA often needs to take some measures to adjust the model and to guide sampling. In this article, we propose a novel EDA which applies the idea of Kalman filtering to revise the modeling data and a learning strategy to improve sampling. The filtering scheme modifies the modeling data set using an estimation error matrix based on historic solution data. During the sampling process, the learning strategy determines the region to sample next based on the sampling outcomes so far, instead of completely random sampling. The proposed EDA also employs a multivariate probabilistic model based on copula function and can quickly reach the promising area in which the optimal solution is likely to be located. A collection of general benchmark functions are used to test the performance of the proposed algorithm. Computational experiments show that the EDA is effective. Note to Practitioners—In many process industries, there exist black-box operation optimization problems and large-scale nonlinear optimization problems with variable coupling. For these problems, it is difficult to establish mechanism models between input and output. However, real-time data can be measured from the system through sensors. We can utilize this process information to optimize the system so as to attain the desired objective. In this article, we propose a novel estimation of distribution algorithm (EDA) which applies a filtering scheme to revise the modeling data and a learning strategy to improve sampling, which can solve the problems with the characteristics of nonlinearity, variable coupling, and large scale. Computational experiments show that the EDA is effective. In the future, the proposed algorithm can be applied to some practical optimization problems such as operation optimization in blast furnace, which is considered as a continuous production process with variable coupling. The algorithm has the potential to help optimizing the process control parameters. [ABSTRACT FROM AUTHOR]

The steelmaking process in the iron and steel industry involves complicated physicochemical reactions. The main aim of steelmaking is to adjust the quality of molten steel. During the steel-tapping process, the temperature and carbon content are the most essential quality indices for end-point prediction. This article presents a novel machine learning framework for the end-point prediction problems in the smelting process. Considering the importance of data representation in modeling, the original data are inputted to a stacked autoencoder (SAE) to extract the essential features in an unsupervised manner. The top layer is then designed as a sparse Bayesian regression (SBR) layer to obtain the predicted mean values and error bars that measure the uncertainty in the prediction. To improve the generalization ability of the prediction model, an intelligent optimization algorithm based on improved differential evolution (DE) is used to optimize the hyperparameters of the model. The main advantage of this model is that the underlying characteristics of the samples can be learned automatically layer by layer, instead of designing them manually. Finally, the effectiveness of the proposed method is verified using real data collected from two steel plants. The experimental results show that the proposed model gives a more precise prediction than other existing models and can provide error bars for the end-point prediction. Note to Practitioners—The end-point prediction is critical to the quality of products produced in the iron and steel production process. Because of the complexity of the physiochemical reactions during smelting, it is difficult to build mechanism models for the complex environment. Generally, expert knowledge and experience are needed for real production. The motivation behind this article was to establish a data-driven model through machine learning methods to solve end-point prediction problems. First, a stacked autoencoder (SAE) was used to extract the essential information from the original data. Second, sparse Bayesian regression (SBR) was applied to the top layer. Thus, the predicted mean values and error bars could be obtained to improve the robustness of the prediction model. Furthermore, an improved differential evolution (DE) algorithm was designed to adaptively optimize the hyperparameters of the model. In the experiments, the proposed method was applied to two real steelmaking production processes. The results verify the effectiveness of the proposed model. This article can be extended to other processes such as continuous casting and reheating furnace. The model can also be generalized for other practical industries to improve the product quality and enable safer production. [ABSTRACT FROM AUTHOR]

We study integrated production- and delivery-scheduling problems that arise in practical make-to-order settings in several industries. In these problems, make-to-order products are first processed in a plant and then delivered to customer sites through two stages of shipping: first, from the plant to a pool point (e.g., a port, a distribution, or a consolidation center) and, second, from the pool point to customer sites. The objective is to obtain a joint schedule of job processing at the plant and two-stage shipping of completed jobs to customer sites to optimize a performance measure that takes into account both delivery timeliness and total transportation costs. We consider two problems in which delivery timeliness is measured by total or maximum lead time of the jobs and study both offline and online versions of these problems. For the offline problems involving a single production line at the plant, we provide optimal dynamic programming algorithms. For the more general offline problems involving multiple production lines at the plant, we propose fast heuristics and analyze their worst-case and asymptotic performances. For the online problems, we propose online algorithms and analyze their competitive ratios. By comparing our offline heuristics with lower bounds using randomly generated test instances, it is shown that these heuristics are capable of generating near-optimal solutions quickly. Using real data from Baosteel's Meishan plant, we also show that our corresponding offline heuristic generates significantly better solutions than Baosteel's rule-based approach. In addition, our computational results on the performance of the online algorithms relative to the offline heuristics generate important methodological insights that can be used by practitioners in choosing a specific solution approach. [ABSTRACT FROM AUTHOR]

This paper proposes a dynamic analytics method based on the least squares support vector machine with a hybrid kernel to address real-time prediction problems in the converter steelmaking process. The hybrid kernel function is used to enhance the performance of the existing kernels. To improve the model’s accuracy, the internal parameters are optimized by a differential evolution algorithm. In light of the complex mechanisms of the converter steelmaking process, a multistage modeling strategy is designed instead of the traditional single-stage modeling method. Owing to the dynamic nature of the practical production process, great effort has been made to construct a dynamic model that uses the prediction error information based on the static model. The validity of the proposed method is verified through experiments on real-world data collected from a basic oxygen furnace steelmaking process. The results indicate that the proposed method can successfully solve dynamic prediction problems and outperforms other state-of-the-art methods in terms of prediction accuracy. Note to Practitioners—With the development of cyber-physical systems, abundant real-time data have been collected from the converter steelmaking process. These data provide an opportunity to solve product quality prediction problems using data-driven models. This paper proposes a dynamic analytics method based on the least squares support vector machine with a hybrid kernel to address this challenging issue. To improve the model’s performance, a differential evolution algorithm is used to optimize its parameters. Because of the fierce physicochemical reaction in the converter furnace, it is difficult for a single-stage model to achieve accurate predictions. Thus, a multistage modeling strategy is proposed to address this difficulty, and a dynamic model based on feedback error is developed to realize real-time prediction. We verify the effectiveness of the proposed method using real data from a basic oxygen furnace (BOF) steelmaking process. The computational results reveal that the proposed method has a higher prediction accuracy than other methods, making it helpful in guaranteeing the specified product quality and in maintaining stable BOF operation. [ABSTRACT FROM AUTHOR]

Abstract In recent years, least squares support vector machines (LSSVMs) with various kernel functions have been widely used in the field of machine learning. However, the selection of kernel functions is often ignored in practice. In this paper, an improved LSSVM method based on self-organizing multiple kernel learning is proposed for black-box problems. To strengthen the generalization ability of the LSSVM, some appropriate kernel functions are selected and the corresponding model parameters are optimized using a differential evolution algorithm based on an improved mutation strategy. Due to the large computation cost, a sparse selection strategy is developed to extract useful data and remove redundant data without loss of accuracy. To demonstrate the effectiveness of the proposed method, some benchmark problems from the UCI machine learning repository are tested. The results show that the proposed method performs better than other state-of-the-art methods. In addition, to verify the practicability of the proposed method, it is applied to a real-world converter steelmaking process. The results illustrate that the proposed model can precisely predict the molten steel quality and satisfy the actual production demand. [ABSTRACT FROM AUTHOR]

Highlights • A mixed integer nonlinear program is formulated for a new integrated steelmaking scheduling problem that incorporates batching decisions and energy constraints. • An enhanced spatial branch and bound algorithm is proposed that outperforms the commercial solvers in solving the studied problem. • A three-phase MILP-NLP decomposition strategy is developed that is able to either work as a node heuristic or run as an independent heuristic. Abstract This paper investigates a practical steelmaking scheduling problem with batching decisions and energy constraints, in which batching is used to decide how to group and sequence a number of jobs to form job groups so as to meet batch production mode. Incorporating energy consideration into the scheduling is motivated by practical demand and the potential to reduce the energy bill through optimal scheduling. Based upon our proposed energy expressions, an MINLP model is formulated and solved using the spatial branch and bound (B&B) algorithm, which is enhanced by the proposed decomposition strategy working as a node heuristic. The benefits of the integrated scheduling and some sensitivity analysis experiments are reported on an illustrative example. Experiments on randomly generated instances show that the energy expressions are superior to the one of Hadera et al. (2015) , the B&B outperforms the state-of-the-art commercial solvers and the decomposition strategy performs well running as an independent heuristic. [ABSTRACT FROM AUTHOR]

Steel-making production is a dynamic process that has the characteristics of high temperature and heat, and a complex reaction mechanism that causes the mechanism model possibly to be unavailable and the system to be a black box. In this article, a dynamic operation optimization (DOO) problem is refined from the basic oxygen furnace (BOF) steel-making process, and the system model is formulated by a data analytics method. This prevents to solve the optimization problem with derivative-based optimization methods. To circumvent these difficulties, a surrogate-model-based derivative-free optimization algorithm is proposed for solving the DOO problem. In order to establish the surrogate model with the least number of function evaluations, a subset selection strategy is designed to find a sparse structure for the optimization problem, based on which a set of simple bases is determined to establish the surrogate model. Moreover, this also reduces the scale of the parameter optimization problem. Numerical experiments on actual production data verify the applicability and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The study proposes a stochastic optimisation approach based on discrete scenarios and the minimax criterion to deal with demand uncertainty in the two‐stage unit commitment problem. To avoid making an over‐conservative decision, the approach is designed to assign a probability‐based weight to each demand scenario and provide a unit commitment schedule that minimises the maximum possible‐weighted generation cost. Unit commitment is determined in the first stage and the economic dispatch under the scenario that corresponds to the maximum possible‐weighted generation cost is determined in the second stage. The problem is formulated as a type of min–max–min mixed integer programming model. By introducing an auxiliary variable, the model is further transformed into a minimisation problem. A Benders decomposition algorithm is developed to solve the problem. The Benders master problem determines the unit commitment decision, while the Benders subproblem determines the dispatch decision. Multiple Benders feasibility cuts are constructed when the Benders subproblem is infeasible. Valid inequalities are derived to improve the lower bound provided by the Benders master problem. Numerical results show the performance of the algorithm. [ABSTRACT FROM AUTHOR]

This paper presents scaled quadratic cuts based on scaling the second-order Taylor expansion terms for the decomposition methods Outer Approximation and Partial Surrogate Cuts for solving convex Mixed Integer Nonlinear Programing problems. The scaled quadratic cut is proved to be a stricter and tighter underestimation for convex nonlinear functions than classical supporting hyperplanes, which results in the improvement of Outer Approximation and Partial Surrogate Cuts based solution methods. We integrate the strategies of scaled quadratic cuts with multi-generation cuts for Outer Approximation and Partial Surrogate Cuts and develop six types of Mixed Integer Nonlinear Programming solution methods with scaled quadratic cuts. These cuts are incorporated in the master problem of the decomposition methods leading to a Mixed Integer Quadratically Constrained Programming problem. Numerical results of benchmark Mixed Integer Nonlinear Programming problems demonstrate the effectiveness of the proposed Mixed Integer Nonlinear Programming solution methods with scaled quadratic cuts. [ABSTRACT FROM AUTHOR]

This paper addresses the short-term scheduling problem for the ethylene cracking process with feedstocks and energy constraints. The cracking production of ethylene is a process with units that have decaying performance, requiring periodic cleanup to restore their performance. Under the condition of limited feedstocks, the production operating mode of the cracking furnaces is to keep yields constant by continuously increasing the coil temperature. We present a hybrid MINLP/GDP formulation based on continuous-time representation for the scheduling problem over a finite time horizon. In order to solve the proposed model, which is reformulated as an MINLP model, an improved outer approximation algorithm with multi-generation cuts and problem-dependent integer cuts are developed to solve real large-scale problems. Numerical examples are presented to illustrate the application of the model. Based on analyzing the optimal solution and sensitivity of the model, some conclusions are obtained to provide useful suggestions for real cracking process production. [ABSTRACT FROM AUTHOR]

This study is motivated by the practices of large iron and steel companies that have steady and heavy demands for bulk raw materials, such as iron ore, coal, limestone, etc. These materials are usually transported to a bulk cargo terminal by ships (or to a station by trains). Once unloaded, they are moved to and stored in a bulk material stockyard, waiting for retrieval for use in production. Efficient storage space allocation and ship scheduling are critical to achieving high space utilization, low material loss, and low transportation costs. In this article, we study the integrated storage space allocation and ship scheduling problem in the bulk cargo terminal. Our problem is different from other associated problems due to the special way that the materials are transported and stored. A novel mixed-integer programming model is developed and then solved using a Benders decomposition algorithm, which is enhanced by the use of various valid inequalities, combinatorial Benders cuts, variable reduction tests, and an iterative heuristic procedure. Computational results indicate that the proposed solution method is much more efficient than the standard solution software CPLEX. [ABSTRACT FROM AUTHOR]

We present a weak finite element method for elliptic problems in one space dimension. Our analysis shows that this method has more advantages than the known weak Galerkin method proposed for multi-dimensional problems, for example, it has higher accuracy and the derived discrete equations can be solved locally, element by element. We derive the optimal error estimates in the discrete H 1 -norm, the L 2 -norm and L ∞ -norm, respectively. Moreover, some superconvergence results are also given. Finally, numerical examples are provided to illustrate our theoretical analysis. [ABSTRACT FROM AUTHOR]

We study the superconvergence of the stable lowest equal-order finite element pair solving the Stokes problem. The superclose property is proved for the interpolation function; then a superconvergence rate of $$O(h^{\frac{3}{2}})$$ -order is obtained for the velocity gradient approximation by using the post-processing technique, and an $$O(h^{\frac{3}{2}})$$ -order error estimate is derived for the pressure approximation. In addition, the asymptotically exact a posteriori error estimate also is given by means of the superconvergence result. Finally, some numerical experiments are provided to illustrate the theoretical analysis. [ABSTRACT FROM AUTHOR]

We propose and analyze the finite volume method for solving the variational inequalities of first and second kinds. The stability and convergence analysis are given for this method. For the elliptic obstacle problem,we derive the optimal error estimate in the H1-norm. For the simplified friction problem,we establish an abstract H1-error estimate, which implies the convergence if the exact solution u ϵ H1(Ω) and the optimal error estimate if u ϵ H11+α(Ω)0 < ⩽ 2. [ABSTRACT FROM AUTHOR]

We present a new stabilized finite volume method for Stokes problem using the lowest order P − P element pair. To offset the lack of the inf - sup condition, a simple jump term of discrete pressure is added to the continuity approximation equation. A discrete inf - sup condition is established for this stabilized scheme. The optimal error estimates are given in the H- and L-norms for velocity and in the L-norm for pressure, respectively. [ABSTRACT FROM AUTHOR]

Container stacking and reshuffling are important issues in the management of operations in a container terminal. Minimizing the number of reshuffles can increase productivity of the yard cranes and the efficiency of the terminal. In this research, the authors improve the existing static reshuffling model, develop five effective heuristics, and analyze the performance of these algorithms. A discrete-event simulation model is developed to animate the stacking, retrieving, and reshuffling operations and to test the performance of the proposed heuristics and their extended versions in a dynamic environment with arrivals and retrievals of containers. The experimental results for the static problem show that the improved model can solve the reshuffling problem more quickly than the existing model and the proposed extended heuristics are superior to the existing ones. The experimental results for the dynamic problem show that the results of the extended versions of the five proposed heuristics are superior or similar to the best results of the existing heuristics and consume very little time. [ABSTRACT FROM AUTHOR]

This paper investigates a new lowcarbon iron-making supply chainplanning problem in the steel industry under the carbon cap and trademechanism, in which a steel company uses carbon emission quota toproduce iron to meet determined demands over the planning horizon,and buys or sells the rights to emit carbon in the carbon tradingmarket. The problem decides optimal carbon trade, raw material purchasing,raw materials and sinters inventory levels, as well as sintering andiron making production schemes, so as to minimize the total cost.A novel mixed integer programming model incorporating carbon emissionreduction into operational decision making is developed and we proposea branch and price algorithm to solve it. The model is decomposedequally into a master problem and two subproblems. The branch andprice algorithm is enhanced by two tailored techniques. First, therelaxation of the master problem is strengthened by introducing twofamilies of valid inequalities. Second, we use a dynamic programmingprocedure to eliminate variables by path reduced cost. We test ouralgorithm on randomly generated data that simulate the practical production,and the computational results over the instances illustrate the effectivenessof the proposed algorithm. In addition, we derive the optimal operationaldecisions, and examine the impacts of carbon cap and carbon priceanalytically and numerically on total cost and carbon emissions. Wemake interesting observations based on numerical results and providemanagerial insight to highlight the opportunity to reduce carbon emissions. [ABSTRACT FROM AUTHOR]

• Webuild a black box operation optimization model for the BOF steelmaking process wherean optimization is integrated into a data analytics model. • We design a DFO algorithm based on trust region framework for solving the operation optimization problem. • In the proposed DFO algorithm, quadratic interpolation and sparse modeling are combinedto determine the surrogate model. Mastering the smelting status and making the material adding strategy are important and challenging issue in the basic oxygen furnace (BOF) steelmaking process. Unfortunately, complex physicochemical reaction with multi-input and multi-output makes the first-principles models unusable. To this end, a black box operation optimization (BBOO) problem integrated black box data analytics modeling and optimization is abstracted from the BOF steelmaking process. Moreover, a derivative free optimization (DFO) method based on the trust region framework is developed to solve the operation optimization problem. In the proposed algorithm, we integrate quadratic interpolation and sparse modeling to construct the surrogate model. Wherein, l 0 -norm optimization, which is solved through converting it into a convex optimization problem with introducing the slack variables, is used to determine the sparse surrogate model. Finally, we apply the proposed DFO algorithm and other DFO algorithms to solve the BBOO problem in BOF steelmaking process. The results demonstrate that the proposed method can be an alternative for providing solutions and insights of material addition in BOF process and the proposed algorithm is comparable. [ABSTRACT FROM AUTHOR]

In the iron and steel industry, the hardness of steel strips is one of the key performance indicators to evaluate strip quality and guide production for the continuous annealing production line (CAPL). However, the hardness cannot be measured online in the actual production process. Consequently, the precise prediction of the strip hardness based on practical data becomes one of the key tasks during production. In this article, a multiobjective sparse nonlinear ensemble learning with evolutionary feature selection (MOSNE-EFS) method is proposed, which is data-driven modeling of the soft sensor. The method mainly consists of two stages: 1) the construction of individual learners based on multiobjective feature selection learning (MOFSL) and 2) the selection and ensemble of individual learners based on sparse nonlinear ensemble learning via differential evolution (SNEL-DE). The final ensemble model obtained by SNEL-DE is used as the prediction model for strip hardness in CAPL. The proposed method is evaluated with industrial production data. Experimental results indicate that the two strategies, i.e., evolutionary feature selection and sparse nonlinear ensemble, are effective in improving the accuracy and robustness of the prediction model, and further comparison results demonstrate the superiority of the MOSNE-EFS model over the other existing methods. Note to Practitioners—Many quality metrics in the iron and steel industry cannot be online checked, which causes great difficulties in process monitoring, control, and operation optimization. The proposed multiobjective sparse nonlinear ensemble learning with evolutionary feature selection method can help practitioners to construct quality prediction models of many other similar production lines, such as hot rolling and cold rolling, and thus, better process monitoring, control, and optimization of product quality can be achieved. [ABSTRACT FROM AUTHOR]

The color-coated steel coil is a high value-added product for steel enterprises, and its production process is affected by multiple factors. How to provide operators with appropriate scheduling schemes is the key to improve the economic benefits of enterprises. In this article, for the scheduling of a single color-coating turn, we establish a multiobjective optimization model that minimizes the number of insertions of transition coils, the thickness jump penalty of adjacent coils, and the switching times of the backup rollers. To address this problem, we propose a piecewise coding approach to ensure that each individual meets the production constraints. Besides, a multiobjective evolutionary algorithm (MOEA) based on decomposition and dynamic local search (D-DLS) strategy is proposed (MOEA/D-DLS). More specifically, the color-coating multiobjective scheduling problem is decomposed into a series of single-objective subproblems and optimized simultaneously. Furthermore, based on the speed of evolution of these subproblems, local search is performed on partial subproblems dynamically. The proposed algorithm is used to solve eight multiobjective scheduling problem instances of color-coating with different scales, and the experimental results demonstrate that the proposed algorithm is very effective compared with four state-of-the-art algorithms. Note to Practitioners—Practical production scheduling problems in iron & steel industry generally need to optimize conflicting objectives simultaneously, which is very hard for practitioners to make appropriate decisions with manual experience. The decomposition-based multiobjective evolutionary algorithm (MOEA) can help practitioners of color-coating scheduling to achieve a set of Pareto optimal decisions with good distribution and tradeoff among three objectives. Since the scheduling of the other production lines shares many similarities with our problem, the proposed model and algorithm can also be applicable to these problems. [ABSTRACT FROM AUTHOR]

We study the effects of higher labor costs on the adoption of industrial robots by Chinese firms. We use the number of robots imported by firms from the customs data to measure the adoption of robots. To address endogeneity concerns, we focus on the effects of higher minimum wages from contiguous city pairs that straddle provincial borders. We find negligible effects of minimum wages on firms' robot adoption over 2004–2007, but much larger and statistically significant effects over 2008–2012. For a 10% increase in the minimum wage over 2008–2012, the probability of a firm adopting robots increases by 0.11 percentage points, compared to an average adoption probability of 0.19% over the same period. The effects of higher minimum wages on robot adoption are stronger for firms with higher productivity, those in the coastal region, those with private ownership, and those from skilled-labor-intensive industries. [ABSTRACT FROM AUTHOR]

Differential evolution is one of the most powerful evolutionary algorithms for single objective optimization problems in the literature. Its application in the multiobjective optimization problems is also very successful, and many kinds of promising multiobjective differential evolution (MODE) algorithms have been proposed in the literature. This paper develops a new variant of MODE with two features. First, a set of personal archives are maintained to evolve the search process instead of a population with a fixed size, and a truncation procedure is used to enhance selection pressure. Second, multiple biased mutation operators incorporating the target solution quality are proposed, and an adaptive selection method is adopted to allocate the mutation operators to solutions. The proposed MODE is referred to as the MODE with personal archive and biased self-adaptive mutation selection (BiasMOSaDE). A set of 31 benchmark multiobjective problems selected from the literature are adopted to evaluate its performance. Computational results illustrate that the proposed BiasMOSaDE is competitive or even superior to several state-of-the-art MODEs in the literature. [ABSTRACT FROM AUTHOR]

This article studies a multiple double-load crane scheduling problem in steel slab yards. Consideration of multiple cranes and their double-load capability makes the scheduling problem more complex. This problem has not been studied previously. We first formulate the problem as a mixed-integer linear programming (MILP) model. A two-phase model-based heuristic is then proposed. To solve large problems, a pointer-based discrete differential evolution (PDDE) algorithm was developed with a dynamic programming (DP) algorithm embedded to solve the one-crane subproblem for a fixed sequence of tasks. Instances of real problems are collected from a steel company to test the performance of the solution methods. The experiment results show that the model can solve small problems optimally, and the solution greatly improves the schedule currently used in practice. The two-phase heuristic generates near-optimal solutions, but it can still only solve comparatively modest problems within reasonable (4 h) computational timeframes. The PDDE algorithm can solve large practical problems relatively quickly and provides better results than the two-phase heuristic solution, demonstrating its effectiveness and efficiency and therefore its suitability for practical use. Note to Practitioners—Bridge cranes are commonly used to move heavy items in manufacturing and logistics systems. Generally, more than one crane runs on a common track. The latest versions of such cranes, such as those used in slab yards in the steel industry, can hold two items simultaneously. Operations scheduling of multiple double-load cranes involves the assignment of tasks to the cranes, the combination of tasks to double-load operations, and sequencing of the tasks, considering the noncrossing constraint between cranes. Effective solution of this complex problem can help to fully utilize the crane capability, increase productivity, and reduce energy consumption. This article models this problem and develops a heuristic solution that combines differential evolution (DE) and DP. Experiment results show that the algorithm is effective and efficient for practical use in slab yards. It may also be applicable to other systems using similar cranes. [ABSTRACT FROM AUTHOR]

The operation optimization problem of furnace process (OOPFP) is to obtain an optimal setting of furnace temperatures to make the reheated slabs have suitable temperature distribution with minimum energy consumption and oxide loss. Since the furnace process is complex, dynamic, and nonlinear, it is difficult to establish a precise process model for the OOPFP. In this paper, firstly, a novel hybrid modeling method combining temperature mechanism with data analytics is proposed to map the relationship of the temperature variables in the furnace process more accurately. The main idea of the hybrid modeling is to compensate for the deviation of original temperature mechanism model through the least squares support vector machine. Then, an improved differential evolution (DE) with feasible region dynamic adjustment strategy and population size gradual shrinking strategy is proposed to solve the OOPFP under hybrid model. Finally, extensive numerical experiments are carried out to demonstrate the effectiveness and accuracy of the hybrid model, and evaluate the performance of the improved DE on solving OOPFP. Experimental results show that the hybrid model is more precise than the original mechanism one, and the proposed DE outperforms other representative algorithms, respectively. [ABSTRACT FROM AUTHOR]

• A novel multi-objective optimization model for oxygen production systems. • A novel DRL-MOEA method for improving the solving efficiency of MOEA. • A surrogate model for training agents of DRL-MOEA in a short time. • Analyses of operation cost and operation mode transition in tradeoff terms. • Analyses of schedules in flexible demand and various electricity prices schemes. The oxygen production system in integrated iron and steel plants is a highly energy-intensive sector producing gaseous oxygen for the manufacturing processes. This study investigates an oxygen production system with cryogenic air separation units (ASU), vaporizers, and liquefiers under frequent demand-changing scenarios and various electricity price contracts. A multi-objective optimization model is established to minimize the total operating cost to reduce energy consumption and simultaneously minimize switching times of operating modes to pursue operational stability. The proposed model not only schedules operation modes of ASUs and on/off modes of vaporizers and liquefiers but also determines the production level of these units. To solve the problem, a multi-objective evolutionary algorithm (MOEA) is proposed, in which the proximal policy optimization, one of deep reinforcement learning (DRL) methods, is incorporated to adaptively select the mating individuals and determine the crossover ratio at each evolutionary iteration. Besides, a surrogate model for optimizing the total operating cost is presented to accelerate the training process of the proposed deep reinforcement learning based multi-objective evolutionary algorithm (DRL-MOEA). The performance of the proposed algorithms is demonstrated using practical instances, and experiment results show that it saves the total operating cost up to 0.86% and decreases the operating modes switches as much as 14.41%, compared to MOEA. The model offers the on-site manager solutions to coordinate the supply and demand with a good trade-off between reducing overall energy consumption and pursuing streamlined operating conditions. [ABSTRACT FROM AUTHOR]

Due to increasing load and penetration of renewables, the electric grid is using time-of-use pricing for industrial customers. Involving energy-intensive processes, steel companies can reduce their production cost by accounting for changes in electricity pricing. In particular, steel companies can take advantage of processing flexibility to make better use of electric power, and thus reduce the energy cost. In this paper, we address a new integrated scheduling problem of multi-stage production derived from the rolling sector of steel production, with consideration of campaign decisions and demand-side management. The problem is formulated as a continuous time mixed-integer nonlinear programming (MINLP) model with generalized disjunctive programming (GDP) constraints, which is then reformulated as a mixed-integer linear programming (MILP) model. Numerical results are presented to demonstrate that the model is computationally efficient and compact. [ABSTRACT FROM AUTHOR]

In this work, we focus on the scheduling of multi-crane operations in an iron and steel enterprise for a two-stage batch annealing process. The first stage is the heating process, and the second stage is the cooling process. To start the heating (cooling) stage, a special machine called a furnace (cooler) must be loaded. The real constraints of no-delay machine unloading are defined as follows: once the heating (cooling) is completed, the furnace (cooler) must be unloaded by crane immediately. The goal is to schedule limited machines (furnaces and coolers) operated by multiple cranes to minimize the completion time of the last annealed coil ( makespan ). We formulate a mixed-integer linear programming model to address this problem. Certain feasible properties are identified to avoid crane conflicts and ensure that the machine unloading no-delay constraints are met. Based on these necessary conditions, we then present a heuristic algorithm with running time in connection with the number of cranes, coils and machines. A lower bound to the problem is also developed. Through theoretical analysis, we show the worst-case bound of our heuristic algorithm. The average performances of the solution approaches are computationally evaluated. The computational results show that the proposed heuristic algorithm is capable of generating good quality solutions. [ABSTRACT FROM AUTHOR]

This work aims to solve the integrated optimization for a complete refinery, which spans from crude oil operations to the refining processes and blending operations. We define a multi-period refinery planning problem by considering the scheduling for crude oil in which the objective function is to maximize the net profit. The optimization procedure simultaneously determines the variables of crude oil scheduling, refinery planning, and blending recipes in each time period. A hierarchical hybrid continuous-discrete time representation is proposed for the integrated optimization problem. This integrated optimization problem yields a Mixed-Integer Nonlinear Programming model for the refinery-wide multi-period optimization. The main contribution of this work is the novel mathematical optimization model proposed for the entire process optimization of refinery production. Computational results with the solvers DICOPT and BARON illustrate the scope of integrated optimization solutions for a real refinery plant with different horizon lengths, demonstrating the tractability, validity, and capability for obtaining near-optimal solutions of the proposed integrated model. • Integrated optimization for whole refinery processes is studied, which covers crude oil operations and refining processes. • Multi-period refinery planning problem by considering scheduling for crude oil is defined. • Hierarchical hybrid continuous-discrete time representation is designed for multi-scale integrated optimization problem. • Novel mathematical optimization model is proposed for entire process optimization of refinery production. • Numerical results demonstrate tractability and validity of the proposed integrated model. [ABSTRACT FROM AUTHOR]

Due to the absence of accurate tools for early detection and successful treatment, lung adenocarcinoma (LUAD) is one of the most aggressive tumors with high morbidity and mortality globally. It is absolutely necessary to investigate the process behind its development and search for new biomarkers that could aid in the early detection of LUAD. There is a correlation between the immune microenvironment of the tumor and the prognosis of lung cancer as well as the efficacy of immunotherapy. Long noncoding RNAs (lncRNAs) have been identified as potential prognostic biomarkers linked to immunological activities. In this study, we identified 1 downregulated lncRNA and 76 upregulated lncRNAs in LUAD samples from TCGA datasets. Among the 77 dysregulated lncRNAs, our attention focused on lncRNA LINC01833 (LINC01833). When compared with nontumor specimens, the level of expression of LINC01833 was shown to be significantly elevated in LUAD samples. In addition, the data of the ROC study revealed that LUAD patients with high LINC01833 expression had an AUC value of 0.840 (95% confidence interval: 0.804 to 0.876). There was a correlation between high LINC01833 expression and an advanced clinical stage. Patients who had a high expression of LINC01833 were shown to have a lower overall survival rate p < 0.001 and a lower disease-specific survival rate (p = 0.004) in comparison to patients who were in the low LINC01833 group, according to the data on survival. In addition, the results of the multivariate analysis revealed that high LINC01833 expression was an independent predictor of poor survival in LUAD. Moreover, the immune analysis revealed that we found that the expression of LINC01833 was positively associated with Th2 cells, aDC, and Tgd, while negatively associated with Mast cells, Tcm, Eosinophils, iDC, DC, Tem, Th17 cells, and pDC. Overall, our data point to the possibility that the unique lncRNA LINC01833 might be employed as a diagnostic and prognostic marker, and as a result, it has a significant impact on clinical practice. [ABSTRACT FROM AUTHOR]

Dysfunction of spermatogenesis is a major complication of diabetes mellitus (DM). This study characterized the protective effects of Dendrobium nobile -derived polysaccharides (DNP) against spermatogenetic dysfunction in mice with streptozotocin (STZ)-induced diabetes. The diabetic mice had lower body and testicular mass, and fewer spermatozoa with a higher incidence of malformation. The testicular histology showed disordered narrow seminiferous tubules covering a smaller area, and fewer spermatogenic cells. Moreover, the qRT-PCR analysis indicated that DM was associated with high expression of the pro-apoptotic factor Bax and low expression of the anti-apoptotic factor Bcl-2 in the testes. The qRT-PCR and immunohistochemical analysis clarified that DM was also associated with low testicular expression of the Sertoli cell (SC) markers GATA-4, WT1, and vimentin, and genes encoding the glycolytic rate-limiting enzymes LDHA, PKM2, and HK2. DNP treatment increased the body and testicular masses, sperm count, and number of spermatogenic cells of the mice, and reduced the proportion of abnormal sperm. DNP also reduced the expression of Bax, and increased that of Bcl-2, GATA-4, WT1, vimentin, LDHA, PKM2, and HK2, in the testes of the diabetic mice. Thus, DNP protects against spermatogenic dysfunction in diabetic mice by inhibiting apoptosis and activating the glycolytic pathway in their testes. • DNP treatment alleviates the histology of the testis and epididymis of diabetic mice. • DNP ameliorates the apoptosis of spermatogenic cells in testes of diabetic mice. • DNP recovers the number and function of testicular Sertoli cells in diabetic mice. • DNP enhances the expression of genes encoding rate-limiting glycolytic enzymes in testes of diabetic mice. [ABSTRACT FROM AUTHOR]

Theoperation optimization problem (OOP) is an important issuein production process control and optimization in process industries,because the desired solution of OOP is the optimal setting for controlvariables, and this setting affects the product quality to a greatextent. In this paper, the OOP in the hot-rolling production processof iron and steel industry is investigated. The OOP lies between theproduction scheduling layer and the process control layer in the integratedautomation system in iron and steel industry, and its main task isto set the optimal values for control variables (i.e., rolling force,exit thickness, etc.) based on the production process constraints.These values are then set as the targets for the process control layer.Different from previous operation optimization of hot rolling processconsidered in the literature, the mathematical model constructed inthis paper considers more practical process constraints such as theramping constraints, to get high-quality products. To efficientlysolve this problem, a hybrid self-adaptive genetic algorithm (HSaGA)is presented. The main feature of the HSaGA is that it utilizes fourdifferent crossover operators and can self-adaptively select the operatorsthat are most appropriate for the current problem. Such a strategycan help to improve the convergence speed and robustness of the geneticalgorithm. Besides this strategy, a solution selection strategy isalso proposed to select parent solutions to perform the crossoveroperation. This strategy divides the entire population into two partsonewith good quality in the objective function value and the other onewith good quality in diversityand then the parent solutionsare selected from them, to improve the search diversity during evolutionand, thus, avoid being trapped in a local optimum. Experiments arecarried out based on benchmark test problems and practical OOP ofthe hot-rolling production process. The computational results showthat the proposed HSaGA is superior to many state-of-the-art evolutionaryalgorithms in the literature for benchmark problems and can obtainbetter results than the empirical method for the OOP of the hot-rollingprocess. [ABSTRACT FROM AUTHOR]

Polyetherimide (PEI) composite is an engineering plastic with excellent performance. After metallization, the metalized PEI composite has some unique properties, such as electromagnetic shielding and high electrical conductivity. In this study, a composite coating of Ni/Cu/Ni was produced on the surface of a PEI composite substrate. Nickel and copper layers were plated by electroless and electrodeposition, respectively. The influences of copper interlayer thickness and heat treatment on the adhesion state of the multilayer coatings on the substrate were studied. The morphology and phase of the composite coatings were determined by optical microscopy, scanning electronic microscopy and X-ray diffraction, respectively. The surface roughness and contact angles of the samples before and after sandblasting were measured by surface profile-meter and goniometer, respectively. The electrical resistivity and adhesion state of the composite coatings were determined by multimeter and the tape test, respectively. The results show that Ni/7.54 μm Cu/Ni and Ni/58.6 μm Cu/Ni multilayer deposits were produced on the substrates. The top surface of the coating with a thin 7.54 μm-Cu interlayer was much rougher than that of a coating with a thick 58.6 μm-Cu interlayer. The initial alkaline electroless nickel layer with 5.8 ± 0.5% phosphorus was composed of a mixture of amorphous and nanocrystalline phases. The Cu interlayer consisted of a face-centered cubic crystalline structure. The phosphorus contents of the top nickel layers for samples #1 and #2 were composed of 6.53 ± 0.4 wt% and 3.52 ± 0.21 wt%, respectively. An increase in Cu interlayer thickness for the composite coating resulted in an increase in electrical resistivity. The average microhardness values of Ni/7.54 μm Cu/Ni PEI composite and the Ni/58.6 μm Cu/Ni PEI composite were about 300 HV0.1 and 244.2 HV0.1, respectively. The adhesion state of the Ni/7.54 μm Cu/Ni and Ni/58.6 μm Cu/Ni PEI composites could be classified as grade 5B and 3B, respectively. The adhesion force of the Ni/58.6 μm Cu/Ni multilayer on the substrate can be improved by heat treatment at 200 °C for 4 h. [ABSTRACT FROM AUTHOR]

Background and Objectives: To present a new method of text-modified food management for patients with dysphagia that integrates the idea of food exchange. In addition to prioritizing nutrition in each recipe, the diet plan emphasizes straightforward preparation methods that balance nutrition and palatability.Methods and Study Design: On the basis of the recommended intake in the Expert Consensus, the design of the texture-modified food incorporates the concept of equivalent food exchange. The plan consists of a staple food, a meat, a vegetable, a snack, and a fruit as the base units, and the volume and nutrient density of each unit is modified to meet the needs of patients with dysphagia.Results: Five categories of standard portions were established, the standard portion of staple foods, milk should be used instead of water during preparation, and carbohydrate components (dextrin) should be added so that each portion provides approximately 200 Kcal of energy. The standard portion of meat, protein components (90% whey) should be added to provide approximately 14 g of protein and 150 Kcal of energy per portion. Two types of standard snacks are recommended, each serving provides 250 Kcal of energy. Vegetables and fruits provide 70 Kcal and 90 Kcal of energy. We compiled 11 recipes representative of the food exchange system and our recipe design priorities (texture modification, sufficient nutrition, color, fragrance and taste).Conclusions: The method is combined theory and practice and can be applied to clinical nutrition work to promote the nutritional intake of patients with dysphagia. [ABSTRACT FROM AUTHOR]

• An exact method for the storage space allocation problem in bulk material stockyards. • A novel MIP formulation tackles the practical challenge of avoiding scattered small fields. • Logic-based Benders approach based on exploitation of operation relationships. • Proposed method effectively solve realistic problem in short time. We consider the storage space allocation problem at an inland bulk stockyard, which aims to find an effective way to store the shapeless materials. Due to the specific storage and handling means in the stockyard, unloading, stacking and reclaiming operations need to be scheduled in an integrated manner. Viewing the space of each stock pad in the yard as a series of unit slots, we first develop a novel MIP formulation which can avoid generating scattered small fields. We then decompose the model by exploiting the relationships among the above operations, and develop a logic-based Benders approach to solve it optimally. [ABSTRACT FROM AUTHOR]

Stroke is a leading cause of motor disability. Acupuncture is an effective therapeutic strategy for poststroke motor impairment. However, its mechanism is still elusive. Twenty-two stroke patients having a right-hemispheric subcortical infarct and 22 matched healthy controls were recruited to undergo diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) scanning. The resting-state fMRI was implemented before and after needling at GB34 (Yanglingquan). The stroke patients presented a substantially reduced fractional anisotropy value in the right superior longitudinal fasciculus (SLF), corticospinal tract, and corpus callosum. The structural integrity of the frontoparietal part of the SLF (SLF-FP) correlated with the motor scores of lower limbs in stroke patients. This corticocortical association bundle originated from the premotor cortex (PM) and the adjacent supplementary motor area (SMA), known as secondary motor areas, and terminated in the supramarginal gyrus (SMG). After acupuncture intervention, the corresponding functional connectivity between the PM/SMA and SMG was enhanced in stroke patients compared with healthy controls. These findings suggested that the integrity of the SLF is a potential neuroimaging biomarker for motor disability of lower limbs following a stroke. Acupuncture could increase the communication between the cortices connected by the impaired white matter tracts, implying the neural mechanism underlying the acupuncture intervention. [ABSTRACT FROM AUTHOR]

During weaning, infants and young animals are susceptible to severe enteric infections, thus inducing intestinal microbiota dysbiosis, intestinal inflammation, and impaired intestinal barrier function. Pectin (PEC), a prebiotic polysaccharide, enhances intestinal health with the potential for a therapeutic effect on intestinal diseases. One 21-d study was conducted to investigate the protective effect of pectin against intestinal injury induced by intraperitoneal injection of Escherichia coli lipopolysaccharide (LPS) in a piglet model. A total of 24 piglets (6.77±0.92 kg BW; Duroc × Landrace × Large White; barrows; 21 d of age) were randomly assigned into three groups: control group, LPS-challenged group, and PEC + LPS group. Piglets were administrated with LPS or saline on d14 and d21 of the experiment. All piglets were slaughtered and intestinal samples were collected after 3 h administration on d21. Pectin supplementation ameliorated the LPS-induced inflammation response and damage to the ileal morphology. Meanwhile, pectin also improved intestinal mucin barrier function, increased the mRNA expression of MUC2, and improved intestinal mucus glycosylation. LPS challenge reduced the diversity of intestinal microbiota and enriched the relative abundance of Helicobacter. Pectin restored alpha diversity and improved the structure of the gut microbiota by enriching anti-inflammatory bacteria and short-chain fatty acids (SCFAs)-producing bacteria, and increased the concentrations of acetate. In addition, Spearman rank correlation analysis also revealed the potential relationship between intestinal microbiota and intestinal morphology, intestinal inflammation, and intestinal glycosylation in piglets. Taken together, these results indicate that pectin enhances intestinal integrity and barrier function by altering intestinal microbiota composition and their metabolites, which subsequently alleviates intestinal injury and finally improves the growth performance of piglets. [ABSTRACT FROM AUTHOR]

• Bubble nucleation, growth, collision, coalescence, and detachment in experiment and simulation. • Evolution of bubble damping oscillations during coalescence with various currents. • A hybrid DPM-VOF-DCTM model on predicting transient multiscale bubble motion. • A DCTM for bridging different scales of the hybrid models of VOF and DPM. The present investigation demonstrates the transient multiscale bubble behaviors including the bubble homogeneous nucleation, continuous growth, disordered collision and coalescence, and disturbed detachment under the anode in the CuSO 4 aqueous solution electrolysis process by experimental and numerical methods. The combined approach of the volume of fluid (VOF) method and discrete phase model (DPM) is applied to solve the macro bubbles' surfaces and track the micro bubbles' trajectories, respectively. The link between the macro continuous and micro dispersed bubbles is achieved by the discrete-continuum transition model (DCTM). The dumbbell and the gourd morphologies are revealed in both experiment and numerical simulation during the binary bubble collision and coalescence. The predicted maximum bubble thickness and maximum bubble coverage present reasonable matches to the experimental data. The results show that the large coalescing bubbles can extend the bubble collision period and increase the fluctuation amplitude of the bubble interface. Increasing the current will increase the bubble collision velocity and shorten the contact time before bubble coalescence, moreover, the recovery time of the oscillating bubble from coalescence to the stable state is also prolonged. Increasing inclined angles of the anode can increase the bubble velocity and decrease the bubble diameter. [ABSTRACT FROM AUTHOR]