Leaf stomatal regulation of water-carbon exchange processes plays a crucial role in the water-carbon cycle. Uncovering the response mechanism of leaf gas exchange to soil water stress is challenging due to the complex...
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Leaf stomatal regulation of water-carbon exchange processes plays a crucial role in the water-carbon cycle. Uncovering the response mechanism of leaf gas exchange to soil water stress is challenging due to the complex effects of both the stomatal regulation ( i.e. , stomatal conductance, g s ) and non-stomatal regulation ( i.e. , photosynthetic carboxylation capacity, V-cmax25). Different from previous studies that achieved stomatal and nonstomatal regulation in stomatal optimization models by linearly simplifying or independently optimizing V-cmax25 , this study hypothesizes that V-cmax25 and g(s) are co-regulated by balancing intercellular CO2 concentration (C-i). By adjusting stomatal opening to minimize water-carbon cost, a stomatal optimization model (SRSC model) that integrates the synergistic regulation of gs and V-cmax25 was developed. Experimental and numerical results show that the SRSC model accurately reproduces the stomatal response to environmental changes, especially for the low soil water potential conditions ( Psi(soil) <-2 MPa) compared to the previous models, which increased the R-2 of g(s) , photosynthetic rate (A(n)), and Ci reaching 2.56 %, 1.97 %, and 9.04 %, respectively. Additionally, the SRSC model reasonably predicted a coordinated decline in g(s) and V-cmax25 and concurrently mitigated the classical models that simulate gs and leaf water potential responses deviating from the actual values under drought conditions. More importantly, the SRSC model revealed that experiencing drought and flooding stresses in rice improved intrinsic water use efficiency by increasing photosynthetic capacity. This study refines the application of the stomatal optimization model and enhances the mechanistic understanding of the stomatal optimization model to a certain extent.
The carbon emissions and cost during the construction phase are significant contributors to the oilfield *** oilfields enter the late stage,the adaptability of facilities *** achieve sustainable development,oilfield r...
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The carbon emissions and cost during the construction phase are significant contributors to the oilfield *** oilfields enter the late stage,the adaptability of facilities *** achieve sustainable development,oilfield reconstruction was usually conducted in discrete rather than continuous *** by economic and sustainability goals,a 3-phase heuristic model for oilfield reconstruction was developed to mine potential locations in continuous *** phase 1,considering the process characteristics of the oil and gas gathering system,potential locations were mined in continuous *** phase 2,incorporating comprehensive reconstruction measures,a reconstruction model was established in discrete *** phase 3,the topology was further adjusted in continuous ***,the model was transformed into a single-objective mixed integer linear programming model using the augmented ε-constraint *** experiments revealed that the small number of potential locations could effectively reduce the reconstruction cost,and the quality of potential locations mined in phase 1 surpassed those generated in random or grid *** studies showed that cost and carbon emissions for a new block were reduced by up to 10.45% and 7.21 %,*** reductions were because the potential locations mined in 1P reduced the number of metering stations,and 3P adjusted the locations of metering stations in continuous space to shorten the pipeline *** an old oilfield,the load and connection ratios of the old metering station increased to 89.7% and 94.9%,respectively,enhancing operation ***,recycling facilitated the diversification of reconstruction measures and yielded a profit of 582,573 ¥,constituting 5.56% of the total *** study adopted comprehensive reconstruction measures and tapped into potential reductions in cost and carbon emissions for oilfield reconstruction,offering valuable insights for future oilfield de
Collaborative distribution is the core of modern logistics, and the collaborative distribution centre is the physical location of distribution. This article aims to study the use of green computing energy management t...
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Collaborative distribution is the core of modern logistics, and the collaborative distribution centre is the physical location of distribution. This article aims to study the use of green computing energy management to promote a collaborative distribution optimization model and algorithm for an intelligent supply chain. A multiobjective genetic algorithm for energy management using green computing and a multiobjective hybrid genetic algorithm based on parallel selection methods are designed and implemented. A joint optimization model of VRP & VFP for logistics distribution is established. Collaborative system design and collaborative system operation inventory control issues are integrated. Considering uncertain demand, a multiobjective mixed-integer programming model of energy management using green computing is established to solve this problem. Experimental research shows that the optimal solution is found before the optimal operation of the 24th-generation collaborative system. The designed functional value of the collaborative system is 66109, and the optimal operating value of the collaborative system is 57348.
Electrifying bus transit systems emerges as a practical solution to environmental degradation resulting from the unprecedented level of mobility nowadays. In the U.S., with the intensified efforts to expand EV infrast...
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Electrifying bus transit systems emerges as a practical solution to environmental degradation resulting from the unprecedented level of mobility nowadays. In the U.S., with the intensified efforts to expand EV infrastructure, a special emphasis is now placed on providing emission-free transit services. This initiative is central to America's push towards a net-zero-emissions future. In response, a growing number of cities have started replacing diesel buses with battery electric buses (BEBs). However, technological, operational, and economic barriers related to charging infrastructure and power supplies make the electrification of bus systems a gradual process, where only a part of the system is electrified at each stage. Moreover, due to the limited battery capacities of BEBs and their stochastic discharge rates influenced by factors like weather, traffic, and road conditions, BEBs often require daytime charging to be able to continue operating throughout the day. Therefore, transit agencies need to develop an integrated strategy that can address various costs of electrification and minimize the planning and operational costs. This study proposes a framework to facilitate the incremental electrification of bus systems. We formulate the problem as a two-stage stochastic mixed-integer linear programming model. The first stage optimizes long-term strategical decisions related to fleet sizing, charging station siting, and charging-station-route assignments under random BEB charging demand and time- of-use electricity tariffs. The second-stage optimizes the charging operations of the BEB fleet for a realized charging demand scenario while maintaining the service schedule for passenger convenience. We also develop a Benders decomposition method to solve the problem with better computational efficiency than existing solvers. To validate the proposed model, we test it on a real-world bus network to design an incremental electrification plan. We show the efficacy of the sol
In this paper, we consider the problem of production capacity estimation for a semiconductor wafer fabrication facility. Capacity estimation involves determining the maximum achievable throughput of a wafer fabricatio...
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In this paper, we consider the problem of production capacity estimation for a semiconductor wafer fabrication facility. Capacity estimation involves determining the maximum achievable throughput of a wafer fabrication facility during a given planning horizon in consideration of both product mix and target cycle time. The wafer fabrication facility (fab) is one of the most complex production systems, consisting of hundreds of process steps for each product as well as thousands of processing machines and re-entrant process flows wherein products must visit the same workcenter multiple times. In this regard, estimating production capacity by modeling the wafer manufacturing process is a challenging problem. To properly capture the dynamics of the process, we propose a flexible-lead-time-based optimization model that considers both the state of work-in-process (WIP) over time and the relationship between WIP levels and lead times. The results of simulation experiments using a real-sized instance demonstrate the advantages of the proposed model over existing alternatives.
BackgroundPatient satisfaction and experience are key outcomes of healthcare and can be computed as powerful measures of service quality. Understand what affects them is essential for service quality improvement. Inve...
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BackgroundPatient satisfaction and experience are key outcomes of healthcare and can be computed as powerful measures of service quality. Understand what affects them is essential for service quality improvement. Investigating whether the care setting (i.e., medical or surgical) can impact the patients' perception of the quality can be also important for the actionability of this data. The aim is to explore which experiential factors should be prioritized to improve patient satisfaction with hospitalization service, using experience items as intermediate results and considering different ***-reported experience measures are used in an Italian region. This study uses the optimization approach to identify factors of healthcare user experience affecting and enhancing *** results confirm that, among the significant determinants of satisfaction, some specific experiential aspects emerged as the potential primary focus to be prioritized in improvement actions. These aspects vary according to the specific departmental *** study presents an optimization model directly informed by healthcare service users, utilizing their insights to drive healthcare delivery improvements. It emphasizes the necessity of not only collect patient perspectives but also applying different methodologies to understand what matters to patients and what interventions could be prioritized, and to strategically use diverse insights to enhance the delivery of healthcare services and patient experience and satisfaction.
To solve the conflict of interests between citizens' travel and public transportation enterprises, and alleviate the pressure of passenger flow at morning and evening peak bus stop, a multimodal combination optimi...
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To solve the conflict of interests between citizens' travel and public transportation enterprises, and alleviate the pressure of passenger flow at morning and evening peak bus stop, a multimodal combination optimization model for public transportation scheduling was proposed. Because the public transport enterprise adopted the conventional dispatching mode, there was a mismatch between passenger flow and transport capacity input. By establishing the public transport dispatching optimization model of the combination of the conventional bus and the inter-district bus, the conventional bus, and the large station express bus, solving the public transport dispatching algorithm, using the vehicle control method of the large passenger flow regional bus stop, we could obtain the control relationship of the arrival time of controllable vehicles. The experimental results show that the Combinatorial optimization scheduling of controllable vehicles enables controllable vehicles to reach the sixth station at the same time, the congestion cost of passengers on the vehicle is zero, and the waiting time of citizens is reduced by 11.15%, which can alleviate the congestion of citizens, improve the public transport capacity, and solve the travel problems of citizens.
Various feedback mechanisms focus on bounded confidence in the consensus reaching process (CRP) for group decision making (GDM) problems. However, confidence level from DMs' subjective cognition can lead to over-c...
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Various feedback mechanisms focus on bounded confidence in the consensus reaching process (CRP) for group decision making (GDM) problems. However, confidence level from DMs' subjective cognition can lead to over-confidence, and thus to have negative effect on CRP. With this idea in mind, this article proposes an objective way to determine bounded confidence levels. In this article, the distribution linguistic preference relation (DLPR) is used to describe decision makers' (DMs') preferences on alternatives. A consensus reaching model with DLPRs in social network GDM (SNGDM) with bounded confidence effect is constructed. In the proposed consensus approach, the objective bounded confidence level is obtained from individual professional performance and social performance, i.e., knowledge degree based on consistency index and entropy measure of DLPRs, and the reliability degree based on trust degree received from other DMs. Then, the acceptable advices based on a bounded confidence-based optimization approach is provided for the identified DMs. Finally, a numerical example and comparative simulation analysis are provided to justify its feasibility and superiority.
The heat dissipation scheme design of power cabin is limited by complex configuration and slow iteration speed. Given the considerable time and computing resources required by numerical experiment, this work proposes ...
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The heat dissipation scheme design of power cabin is limited by complex configuration and slow iteration speed. Given the considerable time and computing resources required by numerical experiment, this work proposes a fast zero dimensional integrating accurate three-dimensional optimization model to calculate the heat dissipation and optimize the thermal management in electric vehicle power cabin. Based on the existing thermal equivalent circuit model, the heat capacity and thermal resistance network among each equipment is established in fast zero-dimensional model, and the output of fast zero-dimensional model is corrected by referring to the accurate initial three-dimensional simulation results. Then, the optimal heat dissipation configuration is searched by zero- dimensional model and validated by experimental data. Results show that the optimization result of fast zero dimensional integrating accurate three-dimensional optimization model is well verified by three-dimensional model. The chip temperature of the power cabin motor controller can be reduced from 551.73 K to 352.31 K after optimizing the number and size of the pin-fins of the motor controller using the proposed model. The time consumption of fast zero dimensional integrating accurate three-dimensional optimization model is 72.0872 h, while the time consumption of three-dimensional model is about 576 h with 224 cores of computer. The proposed model can be used to achieve the purpose of rapidly predicting the temperature change of the complex vehicle design, and provide theoretical reference for the reasonable formulation of the heat dissipation scheme.
Traditional ironmaking process optimization models mostly focus on the compositional properties of the product as constraints in order to optimize the material structure of the ironmaking process, and have not yet con...
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Traditional ironmaking process optimization models mostly focus on the compositional properties of the product as constraints in order to optimize the material structure of the ironmaking process, and have not yet considered the influence of nonlinear quality factors on the dosage optimization scheme. In this paper, an integrated model coupling sinter ore quality performance to cost and energy co-optimization of the ironmaking process is proposed. A mathematical model for the whole-process batching of sinter and blast furnace was established by considering the material and energy flows, and a sinter ore quality prediction model was developed based on a data-based approach. With the lowest cost and energy consumption as the optimization objective, an improved genetic algorithm was used to solve the model and find the group of batching schemes that meet the conventional performance and quality indexes. At the same time to meet the conditions of the optimization scheme of a number of indicators for a comprehensive evaluation, to give to meet the requirements of the optimal program *** the optimization of the test, the cost and energy consumption per ton of iron of the optimal solution are reduced by 5.373 & YEN;/t and 2.129 kgce/t, respectively. Future optimization of the proportion of raw materials for ironmaking, taking into account quality factors, is an important step in the development of lowcarbon and low-cost, especially in the optimization of blast furnace production in the "black box" state.
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