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[Objective] Taking the separation of isobutene/isobutane by conventional and side-stream extractive distillation as the research object, the optimization design of conventional and side-stream extractive distillation was conducted based on decision variables. [Methods] A multi-objective evaluation was conducted from the perspectives of energy, economy, thermodynamics, and environment. The optimal operating parameters for the conventional and side-stream extractive distillation and solvent recovery columns were obtained through the optimization of decision variables. Two extractive distillation processes were optimized with TAC as the target, and the optimal feed stage, extractant feed stage, and number of theoretical and sideline extraction stages were obtained. [Results] The TAC of the optimized conventional and side-stream extractive distillation was 3.26 x 106 and 3.1 x 106 $/year, their TEC was 14 041.9 and 13 717.87 kW, and their CO2 emissions were 4 831 and 4 714 kg/h, respectively. Compared with those of the conventional extractive distillation process, the CO2 emissions, TAC, and TEC of the side-stream extractive distillation were reduced by 2.4%, 5.2%, and 2.4%, respectively. The thermodynamic efficiency increased from 22.4% of the conventional extractive distillation to 23.2% of the side-stream extractive distillation. These results show that, in terms of economic and environmental benefits, the side-stream extractive distillation had greater advantages than the conventional extractive distillation. With two extractive distillation design schemes, students' innovative thinking is stimulated, and new distillation technologies and methods are explored. [Conclusions] Through multi-objective evaluation methods, students' practical ability in distillation operations and awareness of energy saving and consumption reduction are cultivated, enhancing their awareness of energy efficiency and sustainable development concepts, which is conducive to improving their enthusiasm for learning and strengthening their innovative ability in engineering practice. [ABSTRACT FROM AUTHOR]

In many decision tasks, we have a set of alternative choices and are faced with the problem of how to use our latent beliefs and preferences about each alternative to make a single choice. Cognitive and decision models typically presume that beliefs and preferences are distilled to a scalar latent strength for each alternative, but it is also critical to model how people use these latent strengths to choose a single alternative. Most models follow one of two traditions to establish this link. Modern psychophysics and memory researchers make use of signal detection theory, assuming that latent strengths are perturbed by noise, and the highest resulting signal is selected. By contrast, many modern decision theoretic modeling and machine learning approaches use the softmax function (which is based on Luces choice axiom; Luce, 1959) to give some weight to non-maximal-strength alternatives. Despite the prominence of these two theories of choice, current approaches rarely address the connection between them, and the choice of one or the other appears more motivated by the tradition in the relevant literature than by theoretical or empirical reasons to prefer one theory to the other. The goal of the current work is to revisit this topic by elucidating which of these two models provides a better characterization of latent processes in m -alternative decision tasks, with a particular focus on memory tasks. In a set of visual memory experiments, we show that, within the same experimental design, the softmax parameter β varies across m -alternatives, whereas the parameter d of the signal-detection model is stable. Together, our findings indicate that replacing softmax with signal-detection link models would yield more generalizable predictions across changes in task structure. More ambitiously, the invariance of signal detection model parameters across different tasks suggests that the parametric assumptions of these models may be more than just a mathematical convenience, but reflect something real about human decision-making.

Renewable distributed power sources such as wind power and photovoltaic power generation are connected to the power grid, which will bring problems such as increased network loss and reduced power quality. In this paper, a two-layer optimization model for energy storage systems is proposed under large-scale new energy access, and the coupling effects of energy storage planning and operation are considered comprehensively. In the upper layer model, energy storage planning is considered, with the location and capacity of energy storage configuration being the decision variables and the planning cost of the energy storage system being the objective function. The lower layer considers the economy and stability of the grid when the energy storage system is operating, with the output of the energy storage system at each moment as the decision variable and the grid vulnerability, active network loss, and power purchase cost as the objective function. The improved IEEE33 node system has been selected for simulation verification. The results show that the proposed model calculates the optimal capacity configurations of wind power combined energy storage as 0.919 and 0.820 MWh, respectively, and the model can obtain a reasonable energy storage configuration scheme and, in comparing the economic indicators under different scenarios, can obtain the dual storage configuration as the best scheme for the simulation system.

Firstly, on the basis of the multi-objective planning model, we determine the objective function of building energy consumption, the objective function of material cost and the objective function of uncomfortable time percentage. We propose to use a multi-objective genetic algorithm to obtain the Pareto frontier solution for the decision variables of the model by selecting them. The target constraints are determined based on the construction of the building decoration project, and the model of remodeling material cost-schedule-carbon emission’ is constructed. The discrete simulation model of the decoration project is constructed based on the design parameters of the enclosure structure and air conditioning. Considering the structural rigor of the research and analysis, it is necessary to set and explain the parameters of the simulation model, and the materials of the decoration project are studied and analyzed. The results show that on the engineering case analysis, the value of building energy consumption, building cost and the value of uncomfortable time percentage have increased by 23%-45% compared with the value before optimization. In the simulation model, the optimized construction solution’s carbon emission, cost, and duration were reduced by 6.22%, 10.65%, and 45.48%, respectively, compared to the original solution. This study illustrates the aesthetic performance and functional performance on the basis of the process of new materials, which is a guiding reference value for the application of new materials in architectural decoration engineering.

Due to the different complementarity and compatibility of various components in the wind‐solar storage combined power generation system, its energy storage complementary control is very important. In order to ensure the stable operation of the system, an energy storage complementary control method for wind‐solar storage combined power generation system under opportunity constraints is proposed. The wind power output value is obtained. Aiming at the maximum similarity between the total output of wind power storage and the planned output curve, combined with the opportunity constraints and the output and electric quantity constraints of energy storage devices, the energy storage complementary control model is established, and the model is solved by improving the adaptive particle swarm optimization algorithm. The experimental results show that the total output of the wind‐solar storage combined power generation system is consistent with the expected output, and the utilization rate of wind‐solar resources is effectively improved. It has good convergence performance, and can better meet the needs of actual power work. [ABSTRACT FROM AUTHOR]

Abstract Surface irrigation type is one of the most widely used in the world due to its low cost investment. However it is complex method of applying water to soil, because of extreme difficult to achieve good control over the highly variable nature of the movement of water across a soil surface and its infiltration into the soil over a season. This study is attempted to optimize furrow irrigation decision variables at Wonji Shoa sugar estate using field experiment and two simulation models. The field data of experimental site was measured as input for models and simulated using the SIRMOD software and WinSRFR software package. Furrow lengths of 32 m, 48 m and 64 m, slopes of 0.05%, 0.075% and 0.1%and flow rates of 3 l/sec, 4 l/sec and 5 l/sec were analyzed with three replication using volume balance method and two simulation models. The study found optimum decision variables that gave maximum application efficiency and distribution uniformity were slope of 0.1%, furrow length of 32 m and inflow rate of 4 l/sec at cut-off time of 15.79 min. Thus, to improve the performance of furrow irrigation practice, optimal furrow length, inflow rates and cut-off time found by this study should be adopted. Findings from the study would serve as a source of information for use by policy makers and planners during the design and Operation of irrigation development programs. It does not focus on yield related optimization. So, it is open for the future research to conduct yield related optimization of the decision variables.

Abstract Due to the different complementarity and compatibility of various components in the wind‐solar storage combined power generation system, its energy storage complementary control is very important. In order to ensure the stable operation of the system, an energy storage complementary control method for wind‐solar storage combined power generation system under opportunity constraints is proposed. The wind power output value is obtained. Aiming at the maximum similarity between the total output of wind power storage and the planned output curve, combined with the opportunity constraints and the output and electric quantity constraints of energy storage devices, the energy storage complementary control model is established, and the model is solved by improving the adaptive particle swarm optimization algorithm. The experimental results show that the total output of the wind‐solar storage combined power generation system is consistent with the expected output, and the utilization rate of wind‐solar resources is effectively improved. It has good convergence performance, and can better meet the needs of actual power work.

Edge Computing (EC) has made decisions regarding offloading tasks. Also, it might be a challenging task for a gadget of the multi-hop cause of competing network models due to the limited bandwidth available. The literature provides several studies on the issue of the multi-hop cloud environment. Moreover, partial offloading of multi-hop computing and dynamic network management leads to network problems. And inefficient performance measured in terms of task completion time has not been factored into current studies. To reduce the average finishing time of all jobs, this article proposed joint multi-task complete unloading and a network that supports the scheduling issue. Several variable decision factors such as the partial systematic ratio of offloading, Offloading remote devices, task start time, path of routing & dynamic network start time were optimized by the formulated problem. To manage tasks, we use the features inherent in multiple processors with various CPU frequencies. Under the restrictions of power & latency, we determine the appropriate quantity of task data to be handled immediately or distributed to the preferred nodes on the clouds remotely. Our objective is to solve the issue of optimization of task scheduling on a limited quasi-quadratic function. We offer an Improved Offloading and Heuristic Flow Optimization (IOHFO) solution effectively to solve these problems by using semi-definite relaxation. Finally, a simulator can be used to evaluate our proposed unloading system to reduce the (optimal) cost of unloading profiles for various parameters.

Enormous gains in efficiency over the last decade have pushed perovskite solar cells towards the cusp of entering into the mass production phase. Given the solution-phase processability and low energy intensity of several techniques, many techniques have been explored to manufacture these cells and modules. Among them, inkjet printing has shown potential for fabricating all the active layers while also achieving higher efficiencies along with the benefits of lower cost and high material efficiency. In this article, we focus on the application of mathematical modeling of the process and understanding the impacts of different decision variables on key performance indicators at various levels. In particular, the screening of decision variables space and their effect on final key performance indicators related to the complete solar module are discussed, highlighting the effectiveness and limitations of using such mechanistic models for manufacturing system optimization. [Display omitted] • Decision variables screening for inkjet printing of perovskite solar cells. • Application of mechanistic model for inkjet printing to manufacture perovskite solar cells. • Impact analysis on module key performance indicators (KPIs). [ABSTRACT FROM AUTHOR]

Due to the highly competitive market and increasingly stringent environmental regulations, it is paramount to operate chemical processes at their optimal point. In a typical process, there are usually many process variables (decision variables) that need to be selected in order to achieve a set of optimal objectives for which the process will be considered to operate optimally. Because some of the objectives are often contradictory, Multi-objective optimization (MOO) can be used to find a suitable trade-off among all objectives that will satisfy the decision maker. The first step is to circumscribe a well-defined Pareto domain, corresponding to the portion of the solution domain comprised of a large number of non-dominated solutions. The second step is to rank all Pareto-optimal solutions based on some preferences of an expert of the process, this step being performed using visualization tools and/or a ranking algorithm. The last step is to implement the best solution to operate the process optimally. In this paper, after reviewing the main methods to solve MOO problems and to select the best Pareto-optimal solution, four simple MOO problems will be solved to clearly demonstrate the wealth of information on a given process that can be obtained from the MOO instead of a single aggregate objective. The four optimization case studies are the design of a PI controller, an SO2 to SO3 reactor, a distillation column and an acrolein reactor. Results of these optimization case studies show the benefit of generating and using the Pareto domain to gain a deeper understanding of the underlying relationships between the various process variables and performance objectives. [ABSTRACT FROM AUTHOR]

This article presents an alternate solution approach for multi-level multi objective linear fractional programming problems (ML-MOLFPPs) with modifications in the modified fuzzy goal programming (FGP) approach suggested by Lachhwani [1]. In the proposed alternate technique, fractional objective functions at every level are individually considered and converted into individual membership function instead of treating numerator and denominator part of objective function separately for formulation of membership function. The proposed alternate approach for obtaining compromise optimal solution for ML-MOLFPPs is comparatively less computational and efficient. This is demonstrated with the use of proposed technique over numerical example and comparative analysis with earlier method. [ABSTRACT FROM AUTHOR]

Construction of buildings especially high rise building has become one of the most successful business where business people invest large amount of their money in this field. The prosperity of nations and their economy increases by the increase of the size of their construction industry. That's why it's considered one of the most important topic that researchers should focus on how to safely proceed the work. The construction process is divided into a group of small activities that should be put in their right sequence to maintain smooth construction process with the most economic plan of optimized time and cost, Zhang and Ng (2008) [13]. Good planning in the construction projects increases the possibility of the contractor to finish within time and within budget and reduces the chance of expected chaos breaking out. This paper aims to reduce the time and cost for construction projects that experience repetition of the project activities by making use of the phenomena of learning curve that will enhance time and cost savings and also the quality of the project considering the start and end dates (deadline) of the project. This paper is concerned with the continuity of the activity in a way that reduces interruptions for fully and efficiently emphasize smooth learning curve. The paper introduces; (1) detailed scheduling of activities experiencing repetition; (2) optimization of time and cost of the project by specifying the optimal and most recommended number of crews for each activity and the recommended learning rates of the crews performing an activity. Where the learning rates may also express the degree at which the crews performing the task are skilled and intelligent. [ABSTRACT FROM AUTHOR]

This study investigated the use of public and private healthcare systems separately and interactively to find solutions to various problems the healthcare sector has faced including waiting time, cost, and quality of service. Patients, private and public healthcare providers, funders, and governments that make rules about healthcare were defined as components of healthcare systems in this study. Mathematical optimization models were developed by deriving scenarios to balance the components of healthcare systems. In addition to derivation of scenarios related to types of healthcare systems and decision variables, including copayment, treatment cost, reimbursement, and premium, numerical studies for the healthcare components were performed to determine which healthcare systems were useful. The minimum fee payable by patients for the easy access to healthcare schemes was calculated. While there was no remarkable change in the waiting time of the patients in the private healthcare system, the waiting time were reduced by 48.70% in the public healthcare system among the findings. The amount of reimbursement decreased significantly as a result of the interaction of healthcare systems. By reducing the amount of co-payment to the amount charged by the public healthcare system, it has been ensured that private healthcare providers treat more patients. Based on computational analyses, because of the agreements provided between the components, we have advocated that public and private healthcare systems must be unified to provide greater benefits for all the components of healthcare. [ABSTRACT FROM AUTHOR]

Thermal modelling and optimal design of a solar absorption cooling system are presented, and hourly analysis is performed over the period of a year. Three design parameters are considered, then the Real Parameter Genetic Algorithm (RPGA) is applied to obtain the minimum total annual cost. The optimization results show that the solar cooling optimum configuration needs 1630 square meter collectors, a storage tank with a 15000L capacity as well as an absorption chiller with 300kW capacity. The hourly analysis shows that the space temperature fluctuates on average every 62 minutes during June and decreases to 51 minutes in September. In addition, the optimum number of collectors increases 26.67% given a 50% increment in electricity price while it decreases 20% given a 50% decrement in electricity price. Finally a sensitivity analysis on RPGA parameters is performed and results are reported.

Integration of renewable energies such as wind and solar with an energy storage system (ESS) in a distribution network is the interest of current studies in power system engineering. Wind and battery ESS (BESS) are known for their complement and efficient approaches into the distribution networks. The promising of renewable energies for wind and solar in Afghanistan is a motivation for stepping up the power sector of the country by enhancing the power quality as well as self‐dependency in electricity production. In this study, a multi‐objective optimisation technique, non‐dominated sorting genetic algorithm II (NSGA‐II) is proposed for an extensive distribution network in Kabul city considering technical, environmental, and financial control schemes for the network improvement. Three different scenarios with various objective functions are deemed to evaluate their impact on decision variables and network parameters. Furthermore, optimum allocation of the wind turbine and charge/discharge scheduling of BESS are revealed with improvement in performance of the power system. Simulations are deployed in MATLAB® with its application on developed 162‐bus real‐distribution network to demonstrate the effect of different objective function arrangements in each scenario as well as confirming the robustness of the proposed approach. [ABSTRACT FROM AUTHOR]

In many decision tasks, we have a set of alternative choices and are faced with the problem of how to use our latent beliefs and preferences about each alternative to make a single choice. Cognitive and decision models typically presume that beliefs and preferences are distilled to a scalar latent strength for each alternative, but it is also critical to model how people use these latent strengths to choose a single alternative. Most models follow one of two traditions to establish this link. Modern psychophysics and memory researchers make use of signal detection theory, assuming that latent strengths are perturbed by noise, and the highest resulting signal is selected. By contrast, many modern decision theoretic modeling and machine learning approaches use the softmax function (which is based on Luce's choice axiom; Luce, 1959) to give some weight to non-maximal-strength alternatives. Despite the prominence of these two theories of choice, current approaches rarely address the connection between them, and the choice of one or the other appears more motivated by the tradition in the relevant literature than by theoretical or empirical reasons to prefer one theory to the other. The goal of the current work is to revisit this topic by elucidating which of these two models provides a better characterization of latent processes in m -alternative decision tasks, with a particular focus on memory tasks. In a set of visual memory experiments, we show that, within the same experimental design, the softmax parameter β varies across m -alternatives, whereas the parameter d ' of the signal-detection model is stable. Together, our findings indicate that replacing softmax with signal-detection link models would yield more generalizable predictions across changes in task structure. More ambitiously, the invariance of signal detection model parameters across different tasks suggests that the parametric assumptions of these models may be more than just a mathematical convenience, but reflect something real about human decision-making., Competing Interests: Declaration of competing interest The authors declare no conflict of interest.

Introduction: Clinical training programs increasingly use entrustable professional activities (EPAs) as focus of assessment. However, questions remain about which information should ground decisions to trust learners. This qualitative study aimed to identify decision variables in the workplace that clinical teachers find relevant in the elaboration of the entrustment decision processes. The findings can substantiate entrustment decision-making in the clinical workplace.Methods: Focus groups were conducted with medical and veterinary clinical teachers, using the structured consensus method of the Nominal Group Technique to generate decision variables. A ranking was made based on a relevance score assigned by the clinical teachers to the different decision variables. Field notes, audio recordings and flip chart lists were analyzed and subsequently translated and, as a form of axial coding, merged into one list, combining the decision variables that were similar in their meaning.Results: A list of 11 and 17 decision variables were acknowledged as relevant by the medical and veterinary teacher groups, respectively. The focus groups yielded 21 unique decision variables that were considered relevant to inform readiness to perform a clinical task on a designated level of supervision. The decision variables consisted of skills, generic qualities, characteristics, previous performance or other information. We were able to group the decision variables into five categories: ability, humility, integrity, reliability and adequate exposure.Discussion: To entrust a learner to perform a task at a specific level of supervision, a supervisor needs information to support such a judgement. This trust cannot be credited on a single case at a single moment of assessment, but requires different variables and multiple sources of information. This study provides an overview of decision variables giving evidence to justify the multifactorial process of making an entrustment decision. [ABSTRACT FROM AUTHOR]

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