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The rapid increase of aging population poses a great challenge on the public medical resources. Home health care (HHC), which provides the care services for the elderly and patients with mild disease, is an emerging approach to cope with such difficulty. This work formulates a stochastic HHC scheduling and routing problem with skill requirements to minimize total operation time. A stochastic programming model is given to mathematically define it. Then, a hybrid approach combining the genetic algorithm (GA) and simulation optimization with an approximated allocation (AA) rule is designed. The GA aims to search candidate solutions, while the simulation optimization method focuses on improving the efficiency of evaluating them. Numerical results show that the AA rule is more effective than equal allocation and proportional to variance rules in guiding the GA to find promising solutions. As a consequence, the designed approach acquires the better solutions for the problem under consideration. [ABSTRACT FROM AUTHOR]

In recent years, the global resource shortage has become a serious issue. Recycling end-of-life (EOL) products is conducive to resource reuse and circular economy and can mitigate the resource shortage issue. The disassembly of EOL products is the first step for resource reuse. Disassembly activities need tools, and tool deterioration occurs inevitably during the disassembly process. This work studies the influence of tool deterioration on disassembly efficiency. A disassembly line balancing model with the goal of maximizing disassembly profits is established, in which tool selection and assignment is a critical part. A modified discrete migratory bird optimizer is proposed to solve optimization problems. The well-known IBM CPLEX optimizer is used to verify the correctness of the model. Six real-world products are used for disassembly experiments. The popular fruit fly optimization algorithm, whale optimization algorithm and salp swarm algorithm are used for search performance comparison. The results show that the discrete migratory bird optimizer outperforms all three other algorithms in all disassembly instances. [ABSTRACT FROM AUTHOR]

Community Supported Agriculture (CSA), which offers two outstanding advantages, high-quality food and localized production, has come to the fore. In CSA, the output of picking scheduling is the input of delivery scheduling. Hence, only by scheduling the picking stage and distribution stage in a coordinated way can we achieve fresh agricultural products at minimum cost. However, due to asymmetric information in the picking and distribution stage, the integrated scheduling of picking and distribution may lead to an asymmetric optimization problem, which is suitable for solving with an iterative algorithm. Based on this, this work studies an integrated scheduling problem of the picking and distribution of fresh agricultural products with the consideration of minimizing picking and distribution costs as well as maximizing the freshness of orders. First, a nonlinear mixed-integer programming model for the problem under consideration is constructed. Second, a multi-objective multi-population genetic algorithm with local search (MOPGA-LS) is designed. Finally, the algorithm is compared with three multi-objective optimization algorithms in the literature: the non-dominated sorted genetic algorithm-II (NSGA-Ⅱ), the multi-objective evolutionary algorithm based on decomposition (MOEA/D), and the multi-objective evolutionary algorithm based on decomposition that is combined with the bee algorithm (MOEA/D-BA). The comparison results show the excellent performance of the designed algorithm. Thus, the reported model and algorithm can assist managers and engineers in making well-informed decisions in managing the farm operation. [ABSTRACT FROM AUTHOR]

The theoretical research on the percolation mechanism and oil-water relative permeability of low-permeability oil reservoirs is the focus and hotspot of international researchers. Oil-water relative permeability is an important parameter that describes the characteristics of oil-water two-phase flow and is widely used in the dynamic analysis of development and numerical simulation technology in the reservoir. The traditional calculation method of oil-water relative permeability (i.e., the JBN method) is based on the Darcy flow law. When the velocity and the displacement pressure gradient do not follow the linear flow law, there are some errors in the results of oil-water relative permeability calculated by the JBN method. In this paper, the traditional JBN method is improved, and we establish a new processing method of experimental data for oil-water relative permeability considering the effects of nonlinear flow, capillary pressure, and gravity in tight oil reservoirs. The experiments on the flow characteristics of single-phase oil and oil-water relative permeability under nonsteady-state conditions were carried out. The flow velocity and displacement pressure gradient show nonlinear characteristics when single-phase oil is passed through a tight core. At the same time, when the air permeability decreases from 1.92 × 10 − 3 μ m 2 to 0.1 × 10 − 3 μ m 2 , the nonlinear characteristics are becoming more and more obvious. Compared with the traditional JBN method, when the nonlinear flow characteristics are considered, the oil phase relative permeability increases, and the water phase relative permeability is slightly lower. If nonlinear flow characteristics are considered, when the water saturation increases from 0.605 to 0.699, the difference of oil phase relative permeability calculated by the JBN method and this method is gradually decreasing and, with the increase of water saturation, decreases from 0.0029 to 0.0001. In addition, the effects of capillary pressure, gravity, and the nonlinear flow coefficient on the oil-water relative permeability in tight oil reservoirs are studied. The capillary pressure also has a great influence on the relative permeability of the oil phase, and the relative permeability of the oil phase increases with the increase in capillary pressure. In the development process of the low-permeability reservoir, the seepage characteristics of each flow area are different, and the oil-water relative permeability is also different. Therefore, the research results play an important role in guiding the understanding of seepage characteristics of low permeability of tight oil reservoirs. [ABSTRACT FROM AUTHOR]

Disassembly is an essential step in a remanufacturing process via which valuable parts and material of end-of-life (EOL) products can be well reused and resource waste is reduced. Disassembly sequence planning focuses on finding the best disassembly sequence for a given EOL product by considering economic and environmental performance. In a practical disassembly process, one may face a disassembly operation failure risk due to the difficulty of knowing EOL products’ exact information in advance. Despite its importance in impacting disassembly outcomes, the existing work fails to consider it comprehensively. This work proposes a stochastic biobjective DSP problem with the objectives of maximizing disassembly profit and minimizing energy consumption by doing so. A chance-constrained programming model is established, where a chance constraint ensures a fixed confidence level of disassembly failure. To solve it efficiently, a multiobjective multiverse optimization algorithm with stochastic simulation is proposed. Experiments are carried out on four products. Results demonstrate that it outperforms some state-of-the-art algorithms in terms of solution performance. [ABSTRACT FROM AUTHOR]

Ene reductases (ERs) are attractive biocatalysts in terms of their high enantioselectivity and expanded substrate scope. Recent works have proved that synthetic nicotinamide coenzyme biomimetics (NCBs) can be used as easily accessible alternatives to natural cofactors in ER‐catalyzed reactions. However, the structure‐reactivity relationships between NCBs and ERs and influence factors are still poorly understood. In this study, a series of C‐5 methyl modified NCBs were synthesized and tested in the PETNR‐catalyzed asymmetric reductions. The physicochemical properties of these NCBs including electrochemical properties, stability, and kinetic behavior were studied in detail. The results showed that hydrophobic interaction caused by the introduced methyl group contributed to the stabilization of binding conformation in enzyme active site, resulting in comparable catalytic activity with that of NADPH. Molecular dynamics and steered molecular dynamics simulations were further performed to explain the binding mechanism between PETNR and NCBs, which revealed that stable catalytic conformation, appropriate donor‐acceptor distance and angle, as well as free dissociation energy are important factors affecting the activity of NCBs. [ABSTRACT FROM AUTHOR]

Transition metal-free electrochemical approaches for the N-cyanation of secondary amines and the α C-cyanation of tertiary amines have been well established, with products being obtained in moderate to good yields and with good functional group tolerance under ambient conditions. The synthetic application of the protocols has been highlighted through scale-up experiments in a galvanostatic mode. Preliminary mechanistic investigation has confirmed that TBAB played a critical role in N-cyanation transformation and has indicated that the transformation might proceed via a free radical process. [ABSTRACT FROM AUTHOR]

Currently, manufacturing enterprises attach great importance to improving processing quality and customer satisfaction. Hybrid flow shops have widespread applications in real-world manufacturing systems such as steel production and chemical industry. In a practical production process, uncertainty commonly arises due to the difficulty of knowing exact information of facilities and jobs beforehand. In order to improve processing quality and customer satisfaction of manufacturing systems in uncertain environments, this article proposes a stochastic multiobjective hybrid flow shop scheduling problem aiming at maximizing processing quality and minimizing total tardiness, where the processing time of jobs obeys a known random distribution. To describe jobs’ processing quality mathematically, a quality-based cost function is presented, and further a chance-constrained programming approach is used to formulate this problem. Then, a multiobjective artificial bee colony algorithm incorporating a stochastic simulation approach is designed by considering its characteristics. Simulation experiments are performed on a set of instances and several state-of-the-art multiobjective optimization algorithms are chosen as peer approaches. Experiment results confirm that the proposed algorithm has an excellent performance in handling this problem. [ABSTRACT FROM AUTHOR]

Distributed manufacturing scheduling problems have attracted much concern from both industrial and academic areas. Nevertheless, distributed scheduling problems with distribution operations are seldom studied. This work proposes a distributed flexible job shop scheduling problem with distribution operations. A set of jobs is handled at distributed flexible job shops, and then the finished jobs are transported to their corresponding customers following given due dates. First, a mixed integer programming model is established to minimize total tardiness. Second, an ensemble of brain storm optimization and Q-learning methods is developed to solve the formulated model. Six heuristics are hybridized to generate a high-quality initial population. A Q-learning method is devised by fully employing found search information to guide subsequent search processes instead of using fixed parameters as basic brain storm optimization. A variable neighborhood search method combining problem-specific knowledge is designed to further refine the found best individual. At last, the formulated model and method are compared with three state-of-the-art metaheuristics and a mathematical programming solver CPLEX via using a group of problem instances. The results and analysis demonstrate that the developed model and algorithm have more powerful competitiveness in addressing the studied problem. [ABSTRACT FROM AUTHOR]

Introduction: Weeds are one of the main factors affecting crop growth, making weed control a pressing global problem. In recent years, interest in intelligent mechanical weed-control equipment has been growing., Methods: We propose a semantic segmentation network, RDS_Unet, based on corn seedling fields built upon an improved U-net network. This network accurately recognizes weeds even under complex environmental conditions, facilitating the use of mechanical weeding equipment for reducing weed density. Our research utilized field-grown maize seedlings and accompanying weeds in expansive fields. We integrated the U-net semantic segmentation network, employing ResNeXt-50 for feature extraction in the encoder stage. In the decoder phase, Layer 1 uses deformable convolution with adaptive offsets, replacing traditional convolution. Furthermore, concurrent spatial and channel squeeze and excitation is incorporated after ordinary convolutional layers in Layers 2, 3, and 4., Results: Compared with existing classical semantic segmentation models such as U-net, Pspnet, and DeeplabV3, our model demonstrated superior performance on our specially constructed seedling grass semantic segmentation dataset, CGSSD, during the maize seedling stage. The Q6mean intersection over union (MIoU), precision, and recall of this network are 82.36%, 91.36%, and 89.45%, respectively. Compared to those of the original network, the proposed network achieves improvements of 5.91, 3.50, and 5.49 percentage points in the MIoU, precision, and recall, respectively. The detection speed is 12.6 frames per second. In addition, ablation experiments further confirmed the impactful contribution of each improvement component on the overall semantic segmentation performance., Discussion: This study provides theoretical and technical support for the automated operation of intelligent mechanical weeding devices., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Cui, Tan, Bai and Fu.)

Inspired by Nature's ingenuity, considerable progress has been made in recent years to develop chemoenzymatic processes by the integration of environmentally friendly feature of biocatalysis with versatile reactivity of chemocatalysis. However, the current types of chemoenzymatic processes are relatively few and mostly rely on metal catalysts. Here, we report a previously unexplored cooperative chemoenzymatic system for the synthesis of N-heterocycles. Starting from alcohols and amines, benzimidazole, pyrazine, quinazoline, indole, and quinoline can be obtained in excellent yields in water with O 2 as the terminal oxidant. Synthetic bridged flavin analog is served as a bifunctional organocatalyst for the regeneration of cofactor nicotinamide adenine dinucleotide in the bioprocess and oxidative cyclodehydrogenation in the chemoprocess. Compared to the classical acceptorless dehydrogenative coupling strategy, being metal and base free, requiring only water as solvent, and not needing atmosphere protection were observed for the present method, exhibiting a favorable green and sustainable alternative.