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External-rotor dual-armature flux-switching PM (ER-DA-FSPM) machines have high torque density and decent fault tolerance, making them promising candidates for in-wheel machine applications in electric vehicles. The torque output and optimal design parameters of ER-DA-FSPM machines are affected by the stator/rotor torque ratio, which is the focus of this paper. Firstly, this paper analyzes airgap flux density harmonics of ER-DA-FSPM to provide a clear insight into the torque-generation mechanism. Then, this paper investigates the influence of torque ratio on average torque under the same copper loss. It is found that the average torque decreases with torque ratio increasing due to the reduction of the positive torque component generated by the sixth airgap field harmonics and the rise in the negative torque component from the eighth harmonics. Moreover, this paper also provides the optimal parameter recommendation to guide the machine design. The split ratio should increase, and the arc of PMs should decrease for a larger torque ratio, whilst the other parameters are hardly influenced. Next, this paper makes a comparison among the ER-DA-FSPM machine, external rotor flux-switching PM (ER-FSPM) machine, and surface-mounted PM (ER-SPM) machines. It shows that the ER-DA-FSPM machine, with the torque ratio being 2, can lead to a much larger total torque. In addition, in the event of rotor winding failure, which is more possible due to the existence of slip rings than stator winding failure, the stator can still provide an average torque larger than that of ER-SPM machine and 92.0% that of the ER-FSPM machine, respectively. Finally, the theoretical analysis is verified by the experiments. [ABSTRACT FROM AUTHOR]

Environmental nuisance thornbush Prosopis juliflora was utilized as an inexpensive and renewable biomass raw material for the sustainable production of activated carbon. Previously, the sequential muffle furnace-microwave arrangement was effective with acid activation for activated carbon synthesis. However, the intermediate synthesis steps were not optimized. In this work, we have optimized the intermediate steps, viz. chemical impregnation, carbonization, and microwave activation. Sequential optimization for base activation was developed and compared with acid activation. The base-activated carbon (BAC) exhibited a more crystalline nature and faster uptake kinetics than AAC. BAC demonstrated an adsorption capacity of 576 mg/g for 4-nitrophenol (4-NP) surpassing that of optimized acid-activated carbon (AAC) by 45%. The optimal base activation required 1.85 times lower microwave energy than that of the acid activation. BAC exhibited significantly higher BET surface area (1319 m2/g) and micropore volume (0.524 cm3/g) which were about 28% and 26% higher than those of AAC. When compared to biochar obtained from the same thornbush, the BAC exhibited an 11-fold increase in adsorption capacity. The adsorbents could be easily regenerated with ethanol and used up to five cycles. Adsorption using BAC also could achieve 80% COD removal for industrial wastewater, while AAC led to 61% removal. Continuous packed column with BAC revealed a breakthrough time of 3.5 h for industrial effluent while for 500 mg/L 4-nitrophenol, it was 25 h. Prosopis juliflora thornbush, an environmental nuisance, could be converted into a high-capacity adsorbent for environmental remediation after careful sequencing and optimization of the intermediate synthesis steps. [ABSTRACT FROM AUTHOR]

This work considers stochastic optimization problems in which the objective function values can only be computed by a blackbox corrupted by some random noise following an unknown distribution. The proposed method is based on sequential stochastic optimization (SSO), i.e., the original problem is decomposed into a sequence of subproblems. Each subproblem is solved by using a zeroth-order version of a sign stochastic gradient descent with momentum algorithm (i.e., ZO-signum) and with increasingly fine precision. This decomposition allows a good exploration of the space while maintaining the efficiency of the algorithm once it gets close to the solution. Under the Lipschitz continuity assumption on the blackbox, a convergence rate in mean is derived for the ZO-signum algorithm. Moreover, if the blackbox is smooth and convex or locally convex around its minima, the rate of convergence to an $ \epsilon $-optimal point of the problem may be obtained for the SSO algorithm. Numerical experiments are conducted to compare the SSO algorithm with other state-of-the-art algorithms and to demonstrate its competitiveness.

External-rotor dual-armature flux-switching PM (ER-DA-FSPM) machines have high torque density and decent fault tolerance, making them promising candidates for in-wheel machine applications in electric vehicles. The torque output and optimal design parameters of ER-DA-FSPM machines are affected by the stator/rotor torque ratio, which is the focus of this paper. Firstly, this paper analyzes airgap flux density harmonics of ER-DA-FSPM to provide a clear insight into the torque-generation mechanism. Then, this paper investigates the influence of torque ratio on average torque under the same copper loss. It is found that the average torque decreases with torque ratio increasing due to the reduction of the positive torque component generated by the sixth airgap field harmonics and the rise in the negative torque component from the eighth harmonics. Moreover, this paper also provides the optimal parameter recommendation to guide the machine design. The split ratio should increase, and the arc of PMs should decrease for a larger torque ratio, whilst the other parameters are hardly influenced. Next, this paper makes a comparison among the ER-DA-FSPM machine, external rotor flux-switching PM (ER-FSPM) machine, and surface-mounted PM (ER-SPM) machines. It shows that the ER-DA-FSPM machine, with the torque ratio being 2, can lead to a much larger total torque. In addition, in the event of rotor winding failure, which is more possible due to the existence of slip rings than stator winding failure, the stator can still provide an average torque larger than that of ER-SPM machine and 92.0% that of the ER-FSPM machine, respectively. Finally, the theoretical analysis is verified by the experiments.

This work considers stochastic optimization problems in which the objective function values can only be computed by a blackbox corrupted by some random noise following an unknown distribution. The proposed method is based on sequential stochastic optimization (SSO), i.e., the original problem is decomposed into a sequence of subproblems. Each subproblem is solved by using a zeroth-order version of a sign stochastic gradient descent with momentum algorithm (i.e., ZO-signum) and with increasingly fine precision. This decomposition allows a good exploration of the space while maintaining the efficiency of the algorithm once it gets close to the solution. Under the Lipschitz continuity assumption on the blackbox, a convergence rate in mean is derived for the ZO-signum algorithm. Moreover, if the blackbox is smooth and convex or locally convex around its minima, the rate of convergence to an *-optimal point of the problem may be obtained for the SSO algorithm. Numerical experiments are conducted to compare the SSO algorithm with other state-of-the-art algorithms and to demonstrate its competitiveness. [ABSTRACT FROM AUTHOR]

Nowadays, the issue of traffic flow assignment has become an interdisciplinary topic that concerns multiple research areas and branches of science. This work is focussed on the mathematical and computational aspects of the equilibrium traffic assignment problem in the case when route flows are considered to be decision variables. Firstly, we obtain a fixed-point mapping with the explicit contraction operator, which is proven to equilibrate the journey times on feasible routes between a single origin-destination pair of nodes with the quadratic rate. Remarkable that from mathematical perspectives, the developed operator generalizes most path-equilibration operators already exploited by researchers. Secondly, we use the obtained fixed-point procedure to run the sequential path-equilibration algorithm for traffic flow assignment on well-known test urban road networks with arc-additive travel time functions. Our computational results appear to demonstrate higher accuracy of user-equilibrium traffic assignment solutions than the best ones known to us. In other words, developed within this paper sequential path-equilibration algorithm leads to solutions with less goal function values compared to the best solutions for today, according to our knowledge. [ABSTRACT FROM AUTHOR]

Introduction/purpose: In this paper, a new solution for solving a multiobjective integer programming problem with probabilistic multi – objective optimization is formulated. Furthermore, discretization by means of the good lattice point and sequential optimization are employed for a successive simplifying treatment and deep optimization. Methods: In probabilistic multi – objective optimization, a new concept of preferable probability has been introduced to describe the preference degree of each performance utility of a candidate; each performance utility of a candidate contributes a partial preferable probability and the product of all partial preferable probabilities deduces the total preferable probability of a candidate; the total preferable probability thus transfers a multi-objective problem into a single-objective one. Discretization by means of the good lattice point is employed to conduct discrete sampling for a continuous objective function and sequential optimization is used to perform deep optimization. At first, the requirements of integers in the treatment could be given up so as to simply conduct above procedures. Finally, the optimal solutions of the input variables must be rounded to the nearest integers. Results: This new scheme is used to deal with two production problems, i.e., maximizing profit while minimizing pollution and determining a purchasing plan for spending as little money as possible while getting as large amount of raw materials as possible. Promising results are obtained for the above two problems from the viewpoint of the probability theory for simultaneous optimization of multiple objectives. Conclusion: This method properly considers simultaneous optimization of multiple objectives in multi-objective integer programming, which naturally reflects the essence of multi-objective programming, and opens a new way of solving multi-objective problems.

Aiming at the multidisciplinary coupling problem in the design of hypersonic vehicles, especially a series of effects caused by the coupling of aerodynamic heat and structural strength, the development of a multidisciplinary coupling analysis and optimization algorithm has become a key issue in the design process of hypersonic vehicles. To reduce the huge computational cost of multidisciplinary coupling analysis in the process of design optimization, the multifield coupling relationship is analyzed, and a simplified multifield coupling analysis process for hypersonic vehicles is proposed. To solve the problem of the multidisciplinary multiconstrained optimization solution being inefficient and difficult to converge, an optimization algorithm based on a sequential solution for the coupling of the thermal structure, thermal mode, and thermal flutter of hypersonic vehicles is proposed. This algorithm considers the interdependence of multiple disciplines but decouples their constraints through a three-step process. Firstly, the main optimization of the thermal structure is performed. Secondly, the suboptimization of thermal mode and thermal flutter is carried out. Finally, the algorithm returns to the main optimization. Through this three-step nested optimization process, the algorithm iterates until the optimal design point is reached. Numerical examples show that the algorithm can improve optimization efficiency under the premise of ensuring the accuracy of multidisciplinary optimization. [ABSTRACT FROM AUTHOR]

Introduction/purpose: In this paper, a new solution for solving a multiobjective integer programming problem with probabilistic multi – objective optimization is formulated. Furthermore, discretization by means of the good lattice point and sequential optimization are employed for a successive simplifying treatment and deep optimization. Methods: In probabilistic multi – objective optimization, a new concept of preferable probability has been introduced to describe the preference degree of each performance utility of a candidate; each performance utility of a candidate contributes a partial preferable probability and the product of all partial preferable probabilities deduces the total preferable probability of a candidate; the total preferable probability thus transfers a multi-objective problem into a single-objective one. Discretization by means of the good lattice point is employed to conduct discrete sampling for a continuous objective function and sequential optimization is used to perform deep optimization. At first, the requirements of integers in the treatment could be given up so as to simply conduct above procedures. Finally, the optimal solutions of the input variables must be rounded to the nearest integers. Results: This new scheme is used to deal with two production problems, i.e., maximizing profit while minimizing pollution and determining a purchasing plan for spending as little money as possible while getting as large amount of raw materials as possible. Promising results are obtained for the above two problems from the viewpoint of the probability theory for simultaneous optimization of multiple objectives. Conclusion: This method properly considers simultaneous optimization of multiple objectives in multi-objective integer programming, which naturally reflects the essence of multi-objective programming, and opens a new way of solving multi-objective problems. [ABSTRACT FROM AUTHOR]

This work implements a non-linear programming method to synthesize edge weights of an adjacency matrix for a linear parameter varying multi-agent system using bilinear matrix inequalities, which suffer uncertainties. First, convex–concave decompositions are used on the bilinear matrix inequality constraints for nominal H∞synthesis. Then this method is improved to consider uncertainties using integral quadratic constraints with time domain representations. Agents composing the multi-agent system are depicted as longitudinal dynamics of F16 Vista aircraft at different operating conditions, which share their output information to achieve consensus. Topology of the multi-agent system is predefined and edge weights are defined as functions of the decision variable.

• An efficient reliability-based topology optimization method for cross-scale structures. • The introduction of PID controller in sequential strategy achieves fine movement of constraints. • Taking into account the uncertainty of load location, load direction, load amplitude, and material properties. • The introduced PID controller has good applicability and application effect on constrained movement. The continuous improvement of advanced equipment performance has promoted the development of detailed structural design. The detailed design of the structure should meet many requirements such as high reliability, strong robustness, lightweight, and high efficiency. This paper proposes a proportional-integral-derivative-driven efficient reliability-based concurrent topology optimization strategy under the sequential framework. Considering the uncertainty of load position, load direction, load amplitude, and material properties, an efficient reliability-based concurrent topology optimization model is constructed based on the sequential strategy. Based on effectively quantifying the nonlinear characteristics brought by multi-source uncertainty, a constraint refinement movement strategy based on the proportional-integral-derivative controller is proposed, which to some extent overcomes the tedious calculation of uncertainty quantification. This paper provides sensitivity information on macroscopic and microscopic level set functions under displacement constraints. Four numerical examples demonstrate the effectiveness, efficiency, and generalization ability of the proposed method. [ABSTRACT FROM AUTHOR]

In this article, a novel dual-rotor multiple-winding machine (DRMWM) is proposed for the continuous variable transmission (CVT) in hybrid electric vehicles (HEVs). First, a mathematical model of the proposed machine is constructed by using the harmonic modeling method. Then, on the basis of the magnetic field analysis, the operating principle of the DRMWM is deduced. By introducing multiple windings with two different working harmonics, namely the permanent magnet (PM) harmonics, and the magnetic gear harmonics, the proposed DRMWM can cope with various operating conditions of HEVs. To further obtain optimal performance, a finite-element analysis and multiobjective genetic algorithm II combined sequential optimization is carried out for the proposed DRMWM. The proposed optimization method is proven to be more computationally efficient compared to the global optimization method. Finally, a prototype is manufactured and tested in various operating modes. The results validate the simulation and analytical method predictions. Also, it proves that the proposed DRMWM is suitable to work as the CVT in HEVs. [ABSTRACT FROM AUTHOR]

Malaria is an infectious disease affecting a large population across the world, and interventions need to be efficiently applied to reduce the burden of malaria. We develop a framework to help policy-makers decide how to allocate limited resources in realtime for malaria control. We formalize a policy for the resource allocation as a sequence of decisions, one per intervention decision, that map up-to-date disease related information to a resource allocation. An optimal policy must control the spread of the disease while being interpretable and viewed as equitable to stakeholders. We construct an interpretable class of resource allocation policies that can accommodate allocation of resources residing in a continuous domain and combine a hierarchical Bayesian spatiotemporal model for disease transmission with a policy-search algorithm to estimate an optimal policy for resource allocation within the pre-specified class. The estimated optimal policy under the proposed framework improves the cumulative long-term outcome compared with naive approaches in both simulation experiments and application to malaria interventions in the Democratic Republic of the Congo. [ABSTRACT FROM AUTHOR]

This paper proposes a prediction-driven sequential optimization methodology for joint decision-making problems of production-sales-stock in refined oil enterprises. In the proposed prediction-driven sequential optimization methodology, three dynamic nonlinear programming models are first constructed to model the production-sales-stock decision-making problems in refined oil enterprises. Then, the analytical solutions to sequential optimization for production-sales-stock decision-making issues are presented by using the inverse inference method in dynamic programming. Finally, the impact of price and demand prediction of refined oil products on sequential optimization for production-sales-stock decision-making are analyzed using a numerical analysis method. Numerical results demonstrated the significant impact of forecasting results of price and demand of refined oil products on sequential optimization decision-making, indicating that the prediction-driven sequential optimization methodology can be used as an effective tool for joint decision-making of production-sales-stock. [ABSTRACT FROM AUTHOR]

Abstract Optimization of the placement and operational conditions of oil wells plays an important role in the development of the oilfields. Several automatic optimization algorithms have been used by different authors in recent years. However, different optimizers give different results depending on the nature of the problem. In the current study, a comparison between the genetic algorithm and particle swarm optimization algorithms was made to optimize the operational conditions of the injection and production wells and also to optimize the location of the injection wells in a southern Iranian oilfield. The current study was carried out with the principal purpose of evaluating and comparing the performance of the two most used optimization algorithms for field development optimization on real-field data. Also, a comparison was made between the results of sequential and simultaneous optimization of the decision variables. Net present value of the project was used as the objective function, and the two algorithms were compared in terms of the profitability incremental added to the project over twelve years. First, the production rate of the producers was optimized, and then water alternating gas injection wells were added to the field at locations determined by engineering judgment. Afterward, the location, injection rate, and water alternating gas ratio of the injectors were optimized sequentially using the two algorithms. Next, the production rate of the producers was optimized again. Finally, a simultaneous optimization was done in two manners to evaluate its effect on the optimization results: simultaneous optimization of the last two steps and simultaneous optimization of all decision variables. Results showed the positive effect of the algorithms on the profitability of the project and superiority of the particle swarm optimization over the genetic algorithm at every stage. Also, simultaneous optimization was beneficial at finiding better results compared to sequential optimization approach. In the end, a sensitivity analysis was made to specify the most influencing decision variable on the project’s profitability.

Shared Autonomous Electric Vehicles (SAEVs) are pivotal for future transportation, offering both promise and challenges upon integration with the power grid. This symbiosis augments power system flexibility, stability and reliability through Vehicle-to-Grid (V2G) services, and optimize transportation efficiency. However, it amplifies the demand for robust charging infrastructure and electricity power during peak periods. This paper proposes a framework employing a sequential receding horizon optimization approach to manage SAEV mobility and charging dynamics. Focused on maximizing transportation service quality while ensuring power grid stability, the model accommodates dynamic trip requests and electricity generation, utilizing a rolling horizon algorithm. Notably, the study explores the potential of SAEVs in fortifying the integration of renewable energy resources (RES) into the power grid. Our research strives to equip policymakers and system planners with a robust tool for crafting efficient and sustainable future urban transportation and energy systems. • A novel modeling and optimization framework for SAEV-PowerGrid coupled system. • Applied a rolling horizon algorithm to solve the problem with realistic sequential and dynamic input. • Case study applications for downtown Beijing. • Revealed SAEV's significant role in integrating renewable energy resources into the powergrid. [ABSTRACT FROM AUTHOR]

Within the crucial domain of blast furnace ironmaking and sintering, the quality of sinter ore and molten iron holds supreme importance, with direct implications for downstream processes. However, the complexities of utilizing operational experience, understanding mechanisms, leveraging extensive data for precise modeling, and optimizing multiple objectives have persistently posed challenges for engineers. In this research, we propose an novel data/mechanism hybrid-driven modeling and global sequential optimization framework, with three core contributions: (1) Synthesizing field operation insights and mechanistic principles to construct models for molten iron production and energy consumption in ironmaking. (2) Crafting the broad learning approximate-aided subspace identification method (BLASIM), encapsulating the system's dynamic and nonlinear characteristics. This method pioneers a parametric modeling strategy predicated on correlation error for dynamic nonlinear system identification, with its feasibility robustly underpinned by theoretical verification. (3) Streamlining the optimization process by applying expert knowledge to deconstruct a complex multi-objective optimization problem into manageable single-objective tasks. These tasks are addressed sequentially, reflecting operational chronology, and are adeptly resolved using gray wolf optimization algorithm with a sequence relaxant factor. To conclude, the proposed methods are thoroughly validated using real-world blast furnace smelting data, affirming the feasibility and efficiency of modeling accuracy and optimization performance. • Hybrid Modeling: Introduces a hybrid approach combining data and mechanisms. • BLASIM: Crafts a data-centric model, enabling nonlinear state space identification. • Optimization Strategy: Features a global sequential optimization using gray wolf algorithm. • Real-World Validation: Validates methods using real blast furnace data, proving efficacy. [ABSTRACT FROM AUTHOR]

It is critical to optimize the cutting parameters in order to increase the machining efficiency as much as possible while ensuring tool life. Therefore, a model of tool wear in the turning process is established to predict tool wear at any time under various cutting conditions. Thereby, a model of tool life is derived. Considering the influence of uncertain parameters on turning processing, a reliability-based optimization model of turning is established, and the optimization model is solved by the sequential optimization and reliability assessment(SORA)method to obtain the optimal cutting parameters. Finally, an example verifies the advantage and feasibility of this method in the parameter optimization of turning nickel-based superalloy GH4169. [ABSTRACT FROM AUTHOR]

Aim of this study: The major aim of this work was to consistently optimize the production of biomass of the dinoflagellate Karlodinium veneficum and evaluate its extracts biotechnological potential application towards food, nutraceutical or/and pharmaceutical industries. Methods and results: A successful approach of biomass production of K. veneficum CCMP 2936 was optimized along with the chemical characterization of its metabolite profile. Several temperatures (12, 16, 20, 25, 30°C), L1 nutrient concentrations (0.5×, 2×, 2.5×, 3×) and NaCl concentrations (20, 25, 30, 40 g L−1) were tested. The growth rate was maximum at 16°C, 2× nutrient concentration and 40 g L−1 of NaCl; hence, these conditions were chosen for bulk production of biomass. Methanolic extracts were prepared, and pigments, lipids and phenolic compounds were assessed; complemented by antioxidant and anti‐inflammatory capacities, and cytotoxicity. Fucoxanthin and derivatives accounted for 0.06% of dry weight, and up to 60% (w/w) of all quantified metabolites were lipids. Said extracts displayed high antioxidant capacity, as towards assessed via the NO•− and ABTS•+ assays (IC50 = 109.09 ± 6.73 and 266.46 ± 2.25 µgE ml−1, respectively), unlike observed via the O2•− assay (IC25 reaching 56.06 ± 5.56 µgE ml−1). No signs of cytotoxicity were observed. Conclusions: Karlodinium veneficum biomass production was consistently optimized in terms of temperature, L1 nutrient concentrations and NaCl concentration. In addition, this strain appears promising for eventual biotechnological exploitation. Significance and Impact of the Study: This work provides fundamental insights about the growth and potential of value‐added compounds of dinoflagellate K. veneficum. Dinoflagellates, as K. veneficum are poorly studied regarding its biomass production and added‐value compounds for potential biotechnological exploitation. These organisms are difficult to maintain and grow in the laboratory. Thus, any fundamental contribution is relevant to share with the scientific community. [ABSTRACT FROM AUTHOR]

This work presents a novel sequential sampling approach to the multi‐objective reliability‐based design optimization of moment‐resisting steel frames subjected to earthquake excitation. The optimization problem is formulated with two objective functions, namely, the total mass and the energy dissipated by beam members of the frame, and subject to uncorrelated probabilistic constraints on dynamic responses under the effects of correlated random parameters of floor masses, external loads, and material properties. The dynamic responses for a small number of designs are found by nonlinear response history analysis and further approximated by Gaussian process (GP) models to mitigate the computational burden during the optimization process. Approximate solutions sorted among existing candidate solutions are updated after each optimization iteration using discrete random local and global searches. The GP models are also refined after each optimization iteration by specifying new sampling points that lie on the Pareto front of a bi‐objective deterministic maximization problem formulated for the improvement in the current approximate solutions and the feasibility of the new sampling points. As demonstrated in a test problem, the new sampling points tend to distribute in the neighborhood of the exact solutions, thereby underpinning a quick termination as well as the robustness of the proposed method. Optimization results from the test problem and a design example show that good approximate solutions are always obtained as the solution quality converges. [ABSTRACT FROM AUTHOR]

Chemical processes usually exhibit complex, high-dimensional, time-varying, and non-Gaussian characteristics, and the diagnosis of faults in chemical processes is particularly important. However, many current fault diagnosis methods do not consider the temporal correlation of process data, feature selection, and feature sequence arrangement. To solve this problem, this paper presents a fault diagnosis method using a dynamic convolutional neural network, based on a genetic algorithm (GA), for optimizing a feature sequence. First, the input data are transformed into a two-dimensional matrix by adding the dimension of time characteristics. Second, the GA is used to select the features, and the sequence of the selected features is optimized. Finally, the optimized feature sequence is input into the convolutional neural network (CNN) to obtain the final diagnosis results. The Tennessee Eastman chemical process is used for experimental analysis, and the proposed model is compared with the weighted cascade forest, deep belief network (DBN), optimized DBN, long short-term memory + CNN and feature selection using random forest models. The experimental results show that the proposed model has higher diagnostic accuracy. The average diagnosis rate of 20 faults is found to be 89.72%. [ABSTRACT FROM AUTHOR]

Sequential optimization of bioprocess nutritional conditions for production of glutaminase-near-free L-asparaginase by Aspergillus candidus UCCM 00117 was conducted under shake flask laboratory conditions. Catalytic and anti-cancer activities of the poly-peptide were evaluated using standard in vitro biochemical methods. Medium nutrients were selected by one-factor-at-a-time (OFAT) approach while Plackett–Burman design (PBD) screened potential factors for optimization. Path of steepest ascent (PSA) and response surface methodology (RSM) of a Min-Run-Res V fractional factorial of a central composite rotatable design (CCRD) were employed to optimize factor levels towards improved enzyme activity. A multi-objective approach using desirability function generated through predictor importance and weighted coefficient methodology was adopted for optimization. The approach set optimum bioprocess conditions as 49.55 g/L molasses, 64.98% corn steep liquor, 44.23 g/L asparagine, 1.73 g/L potassium, 0.055 g/L manganese and 0.043 g/L chromium (III) ions, at a composite desirability of 0.943 and an L-asparaginase activity of 5216.95U. The Sephadex-200 partially-purified polypeptide had a specific activity of 476.84 U/mg; 0.087U glutaminase activity, 36.46% yield and 20-fold protein purification. Anti-cancer activity potentials of the catalytic poly-peptide were dose-dependent with IC50 (µg/mL): 4.063 (HL-60), 13.75 (HCT-116), 15.83 (HeLa), 11.68 (MCF-7), 7.61 (HepG-2). The therapeutic enzyme exhibited 15-fold more cytotoxicity to myeloid leukemia cell line than to normal (HEK 238 T) cell. Optimum temperature and pH for activity were within physiological range. However, significant interactions between exposure time and levels of each of temperature and pH made interpretations of residual enzyme activities difficult. The manganese-dependent L-asparaginase from Aspergillu s candidus UCCM 00117 is recommended for further anticancer drug investigations. [ABSTRACT FROM AUTHOR]

In this paper, exploring multicarrier waveforms, we investigate the performances of co-existence between radar and communication systems. Performances are measured in terms of radar mutual information (RMI) for the radar system and communication data rate (CDR) for the communication system. Two joint design schemes are proposed based on isolation and sharing, respectively. Firstly, for the isolation-based scheme, radar and communication signals are transmitted/received on the isolated bands. Two independent problems of RMI/CDR maximization are proposed and the optimal power allocation solutions can be characterized using Karush-Kuhn-Tucker (KKT) optimality conditions. This scheme undertakes the advantage of interference avoidance and easy implementation. Secondly, for the sharing-based scheme, radar and communication signals can be transmitted/received on the same band and the RMI/CDR maximization problems are jointly solved. The optimal power allocation solutions are optimized by the proposed sequential optimization algorithm. Compared with the isolation-based scheme, this scheme is more flexible and brings additional gain on performance at the price of higher complexity. Finally, simulation results are provided to analyze and discuss the performance and validate the theoretical results.

The regulations for diesel vehicles are expected to become increasingly more stringent. However, how to coordinate eco-driving, the diesel engine and the urea selective catalytic reduction (urea-SCR) system for fuel economy improvement and emissions reduction remains a formidable challenge. In this paper, a sequential optimization control method with three stages is designed. In stage I, the optimal driving force and braking force are obtained by solving a nonlinear optimization problem of tracking the vehicle velocity profile. In stage II, a real-time reference estimation model is designed to provide the optimal ammonia coverage ratio target. In stage III, to implement the driving force requirement and ammonia coverage ratio target, an integrated engine and urea-SCR system control method and a distributed method are proposed, respectively. The distributed method consists of a fuel injection controller utilizing a data-driven predictive method and a NH3 dosing controller utilizing nonlinear model predictive control (NMPC). The control model of the integrated method is represented by the first-order ammonia coverage ratio dynamics only. The results show that the fuel consumption is improved by 5.3% and the PM emission is reduced by 11.56% during the partial transient acceleration process, and that the fuel consumption and emissions of the integrated control method can accomplish the level of the distributed method and achieve the trade-off between the multi-objective. However, the integrated control method incurs an average computational time penalty of 15.47%.

This paper proposes a prediction-driven sequential optimization methodology for joint decision-making problems of production-sales-stock in refined oil enterprises. In the proposed prediction-driven sequential optimization methodology, three dynamic nonlinear programming models are first constructed to model the production-sales-stock decision-making problems in refined oil enterprises. Then, the analytical solutions to sequential optimization for production-sales-stock decision-making issues are presented by using the inverse inference method in dynamic programming. Finally, the impact of price and demand prediction of refined oil products on sequential optimization for production-sales-stock decision-making are analyzed using a numerical analysis method. Numerical results demonstrated the significant impact of forecasting results of price and demand of refined oil products on sequential optimization decision-making, indicating that the prediction-driven sequential optimization methodology can be used as an effective tool for joint decision-making of production-sales-stock.

We propose a novel antenna clustering-based method for simultaneous wireless information and power transfer (SWIPT) in a multiple-input multiple-output (MIMO) full-duplex (FD) system. For a point-to-point communication set up, the proposed method enables a wireless device with multiple antennas to simultaneously transmit information and harvest energy using the same time-frequency resources. And the energy transmitting device with multiple antennas simultaneously receives information from the energy harvesting (EH) device. This is achieved by clustering the antennas into two MIMO subsystems: one for information transmission (IT) and another for EH. Furthermore, the self-interference (SI) signal at the EH device caused by the FD mode of operation is harvested by the device. For implementation-friendly antenna clustering and MIMO precoding, we propose two methods: (i) a sub-optimal method based on relaxation of objective function in a combinatorial optimization problem, and (ii) a hybrid deep reinforcement learning (DRL)-based method. For the proposed DRL solution, we design a hybrid discrete/continuous action agent that jointly clusters the MIMO antennas between EH and IT, and at the same time, find the best values for MIMO precoding matrices at both devices. This is achieved by using two interacting agent learning subsystems, namely, deep double Q-learning (DDQN), for antenna clustering and deep deterministic policy gradient (DDPG), for MIMO precoding. The effect of imperfect CSI is also studied and investigated. Finally, we study the performances of the two implementation methods and compare them with the conventional time switching-based simultaneous wireless information and power transfer (SWIPT) technique. Our findings show that the proposed MIMO clustering-based SWIPT method gives a significant improvement in spectral efficiency compared to the time switching-based SWIPT method. In particular, the DRL-based method provides the highest spectral efficiency. Besides, the numerical results show that, for the considered system set up, the number of antennas in each device should exceed three to mitigate self-interference to an acceptable level. [ABSTRACT FROM AUTHOR]

Optimization of the placement and operational conditions of oil wells plays an important role in the development of the oilfields. Several automatic optimization algorithms have been used by different authors in recent years. However, different optimizers give different results depending on the nature of the problem. In the current study, a comparison between the genetic algorithm and particle swarm optimization algorithms was made to optimize the operational conditions of the injection and production wells and also to optimize the location of the injection wells in a southern Iranian oilfield. The current study was carried out with the principal purpose of evaluating and comparing the performance of the two most used optimization algorithms for field development optimization on real-field data. Also, a comparison was made between the results of sequential and simultaneous optimization of the decision variables. Net present value of the project was used as the objective function, and the two algorithms were compared in terms of the profitability incremental added to the project over twelve years. First, the production rate of the producers was optimized, and then water alternating gas injection wells were added to the field at locations determined by engineering judgment. Afterward, the location, injection rate, and water alternating gas ratio of the injectors were optimized sequentially using the two algorithms. Next, the production rate of the producers was optimized again. Finally, a simultaneous optimization was done in two manners to evaluate its effect on the optimization results: simultaneous optimization of the last two steps and simultaneous optimization of all decision variables. Results showed the positive effect of the algorithms on the profitability of the project and superiority of the particle swarm optimization over the genetic algorithm at every stage. Also, simultaneous optimization was beneficial at finiding better results compared to sequential optimization approach. In the end, a sensitivity analysis was made to specify the most influencing decision variable on the project's profitability. [ABSTRACT FROM AUTHOR]

Variable-fidelity (VF) surrogate models have attracted significant attention recently in simulation-based design because they can achieve a desirable accuracy at a reasonable cost by making use of the data from both low-fidelity (LF) and high-fidelity (HF) simulations. To facilitate the usage of VF surrogate models assisted efficient global optimization, there are still challenging issues on (1) how to construct the VF surrogate model for simulations with variable-fidelity levels under the non-nested sampling data, (2) how to determine the location and fidelity level of the samples simultaneously, and (3) how to handle constraints when VF surrogate models are also used for constraints. In this work, a variable-fidelity probability of improvement (VF-PI) method is proposed for computationally expensive black-box problems. First, a multi-level generalized Co-Kriging (GCK) model, which is extended from the two-level GCK model, is developed for VF surrogate modeling of simulations with three or more levels of fidelities under non-nested sampling data. Second, to determine the location and fidelity level of the sequential samples, an extended probability of improvement (EPI) function is developed. In EPI function, the model correlation and cost ratio between the LF and HF models, together with the sample density, are considered. Third, the probability of satisfying the constraints is introduced and combined with the EPI function, enabling the proposed approach to handle VF optimization problems with constraints. The comparison results illustrate that the proposed VF-PI method is more efficient and robust than the four compared methods on the illustrated cases. [ABSTRACT FROM AUTHOR]

Directly solving time-dependent reliability-based design optimization (TRBDO) with aleatory and epistemic uncertainties is time-demanding, which limits its engineering application. By treating aleatory and epistemic uncertainties with probability and evidence variables respectively, an advanced decoupling method named sequential optimization and unified time-dependent reliability analysis (SOUTRA) is proposed in this work. By the SOUTRA, the original nested optimization process is solved by a sequence of unified time-dependent reliability analysis, updated reliability index target estimation and deterministic optimization. Only few numbers of the unified time-dependent reliability analysis are required to derive the optimum by the SOUTRA; thus, it is highly efficient. Furthermore, in order to construct the deterministic optimization, a new probability transformation method named focal element midpoint (FEM) is established to convert the evidence variable into a random one. FEM can avoid the issues of uniformity approach and equal areas method, and both are used in the existing probability transformation. Several numerical and engineering applications are introduced to illustrate the effectiveness of the proposed SOUTRA. [ABSTRACT FROM AUTHOR]

Tooth loss from periodontal disease is a major public health burden in the United States. Standard clinical practice is to recommend a dental visit every six months; however, this practice is not evidence-based, and poor dental outcomes and increasing dental insurance premiums indicate room for improvement. We consider a tailored approach that recommends recall time based on patient characteristics and medical history to minimize disease progression without increasing resource expenditures. We formalize this method as a dynamic treatment regime which comprises a sequence of decisions, one per stage of intervention, that follow a decision rule which maps current patient information to a recommendation for their next visit time. The dynamics of periodontal health, visit frequency, and patient compliance are complex, yet the estimated optimal regime must be interpretable to domain experts if it is to be integrated into clinical practice. We combine nonparametric Bayesian dynamics modeling with policy-search algorithms to estimate the optimal dynamic treatment regime within an interpretable class of regimes. Both simulation experiments and application to a rich database of electronic dental records from the HealthPartners HMO shows that our proposed method leads to better dental health without increasing the average recommended recall time relative to competing methods. for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement. [ABSTRACT FROM AUTHOR]

To increase the range of applicability of decoupling strategies for reliability-based design optimization (RBDO), a sequential optimization and moment-based reliability assessment (SOMRA) is proposed. In this approach, a moment method based on the univariate dimension reduction method (UDRM) and probability density function (PDF) estimation is employed. Meanwhile, a corresponding mathematical model and a PDF-based method of calculating the shifting scalar are developed to decouple the reliability assessment from the optimization process. The shifting scalar is corrected according to the nonlinear degree of the limit state surface of the performance function before reconstructing the mathematical model for the next iteration of optimization. This approach uses statistical moments to check whether the constraints are active, and rather than assessing the reliability and calculating the shifting scalars for all constraints, only the active constraints are considered for the PDF estimation and shifting scalar calculation. Three numerical examples and an automobile crashworthiness lightweight design problem are presented to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

With the recent CyberKnife treatment planning system (TPS) upgrade from Precision 1.0 to Precision 2.0, the new VOLO optimizer was released for plan optimization. The VOLO optimizer sought to overcome some of the limitations seen with the Sequential optimizer from previous TPS versions. The purpose of this study was to investigate the clinical impact of the VOLO optimizer on treatment plan quality and clinical treatment efficiency as compared to the Sequential optimizer. Treatment plan quality was evaluated in four categories of patients: Brain Simple (BS), Brain Complex (BC), Spine Complex (SC), and Prostate (PC). A total of 60 treatment plans were compared using both the Sequential and VOLO optimizers with Iris and MLC collimation with the same clinical constraints. Metrics evaluated included estimated treatment time, monitor units (MUs) delivered, conformity index (CI), and gradient index (GI). Furthermore, the clinical impact of the VOLO optimizer was evaluated through statistical analysis of the patient population treated during the 4 months before (n = 297) and 4 months after (n = 285) VOLO introduction. Significant MU and time reductions were observed for all four categories planned. MU reduction ranged from −14% (BS Iris) to −52% (BC MLC), and time reduction ranged from −11% (BS Iris) to −22% (BC MLC). The statistical analysis of patient population before and after VOLO introduction for patients using 6D Skull tracking with fixed cone, 6D Skull tracking with Iris, and Xsight Spine tracking with Iris were −4.6%, −22.2%, and −17.8% for treatment time reduction, −1.1%, −22.0%, and −28.4% for beam reduction and −3.2%, −21.8%, and −28.1% for MU reduction, respectively. The VOLO optimizer maintains or improves the plan quality while decreases the plan complexity and improves treatment efficiency. We anticipate an increase in patient throughput with the introduction of the VOLO optimizer. [ABSTRACT FROM AUTHOR]

This paper proposes a methodological approach for the multi-objective optimization of steel towers made from prefabricated cylindrical stacks that are typically used in the oil and gas sector. The goal is to support engineers in designing economical products while meeting structural requirements. The multi-objective optimization approach involves the minimization of the weights and costs related to the manufacturing and assembly phases. The method is based on three optimization levels. The first is used in the preliminary design phase when a company receives a request for proposal. Here, minimal information on the order is available, and the time available to formulate an offer is limited. Thus, parametric cost models and simplified 1-D geometries are used in the optimization loop performed by genetic algorithms. The second phase, the embodiment design phase, starts when an offer becomes an order based on the results of the first stage. Simplified shell geometries and advanced parametric cost models are used in the optimization loop, which present a restricted problem domain. In the last phase involving detailed design, a full 3-D computer-aided design model is generated, and specific finite-element method simulations are performed. The cost estimations, given the high levels of detail considered, are analytic and are performed using dedicated software. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to suggest and develops a global sensitivity analysis-assisted multi-level sequential optimization method for the heat transfer problem. Design/methodology/approach: Compared with other surrogate-assisted optimization methods, the distinctive characteristic of the suggested method is to decompose the original problem into several layers according to the global sensitivity index. The optimization starts with the several most important design variables by the support vector regression-based efficient global optimization method. Then, when the optimization process progresses, the filtered design variables should be involved in optimization one by one or the setting value. Therefore, in each layer, the design space should be reduced according to the previous optimization result. To improve the accuracy of the global sensitivity index, a novel global sensitivity analysis method based on the variance-based method incorporating a random sampling high-dimensional model representation is introduced. Findings: The advantage of this method lies in its capability to solve complicated problems with a limited number of sample points. Moreover, to enhance the reliability of optimum, the support vector regression-based global efficient optimization is used to optimize in each layer. Practical implications: The developed optimization tool is built by MATLAB and can be integrated by commercial software, such as ABAQUS and COMSOL. Lastly, this tool is integrated with COMSOL and applied to the plant-fin heat sink design. Compared with the initial temperature, the temperature after design is over 49°. Moreover, the relationships among all design variables are also disclosed clearly. Originality/value: The D-MORPH-HDMR is integrated to obtain the coupling relativities among the design variables efficiently. The suggested method can be decomposed into multiplier layers according to the GSI. The SVR-EGO is used to optimize the sub-problem because of its robustness of modeling. [ABSTRACT FROM AUTHOR]

Universal generating function ( UGF) method has great advantages in dealing with probabilistic analysis problems with non-normal random variables and highly non-linear performance functions. A meth¬od of sequential optimization and reliability assessment based on universal generating function (UGF-SO¬RA )is proposed to improve the optimization accuracy of structural reliability. In the porposed method, the high precision reliability analysis method and the offset vector solving strategy are introduced for opti¬mization, and the iteration process is divided into three parts. In the first step, the least square method is used to fit the response surface regression model of migration function, and the offset vectors are solved according to the model and the permissible reliability index. In the second step, according to the offset vector, the deterministic optimization is completed and the current design points are obtained. In the third step, the reliability analysis and evaluation are carried out by UGF method. Furthermore, the miUniversal generating function ( UGF) method has great advantages in dealing with probabilistic analysis problems with non-normal random variables and highly non-linear performance functions. A meth¬od of sequential optimization and reliability assessment based on universal generating function (UGF-SO¬RA )is proposed to improve the optimization accuracy of structural reliability. In the porposed method, the high precision reliability analysis method and the offset vector solving strategy are introduced for opti¬mization, and the iteration process is divided into three parts. In the first step, the least square method is used to fit the response surface regression model of migration function, and the offset vectors are solved according to the model and the permissible reliability index. In the second step, according to the offset vector, the deterministic optimization is completed and the current design points are obtained. In the third step, the reliability analysis and evaluation are carried out by UGF method. Furthermore, the migration function is reconstructed according to the relevant constraints. It is shown through the example a¬nalysis that the proposed method is used to ensure the efficiency of solution, but also improve the accura¬cy of optimization. It also solves the problem that the optimization results can^ converge when the per¬formance function is highly non-linear. [ABSTRACT FROM AUTHOR]

• A flexible prior distribution is introduced to general probabilistic RUL prediction. • More probabilistic lifetime density distributions and confidence intervals are made. • RUL prediction metrics are well improved by optimized prior distribution parameters. • Considerable performance of the proposed method are verified by comparative results. In this study, a deep learning-based probabilistic remaining useful life (RUL) prediction model is proposed to improve the strong prior limitations of traditional probabilistic RUL prediction methods through a flexible prior distribution and strategy for sequential optimization of hyperparameters with regularization factor. It enables output richer probabilistic lifetime density distributions and confidence intervals with various parameters and overcome the problem of poor accuracy of short RUL predictions to some extent. Eventually, the model is effectively validated on a benchmark dataset, and the experimental results show that the probabilistic lifetime prediction model with optimized prior distribution parameters significantly improves prediction performance and demonstrates good learning performance and robustness of test results compared with traditional point estimation methods and parameter-free models. This study informs maintenance decisions and reliability assessments in engineering systems and guides the research and application of probabilistic-based prediction methods in deep learning framework. [ABSTRACT FROM AUTHOR]

Optimization under uncertainty requires proper handling of those input parameters that contain scatter. Scatter in input parameters propagates through the process and causes scatter in the output. Stochastic methods (e.g. Monte Carlo) are very popular for assessing uncertainty propagation using black-box function metamodels. However, they are expensive. Therefore, in this article a direct method of calculating uncertainty propagation has been employed based on the analytical integration of a metamodel of a process. Analytical handling of noise variables not only improves the accuracy of the results but also provides the gradients of the output with respect to input variables. This is advantageous in the case of gradient-based optimization. Additionally, it is shown that the analytical approach can be applied during sequential improvement of the metamodel to obtain a more accurate representative model of the black-box function and to enhance the search for the robust optimum. [ABSTRACT FROM AUTHOR]

A cooler network plays an important role in a heat exchanger network (HEN) in aspects of energy and water conservation, and economic performance of the whole process. However, most studies focus on the circulating cooling water system (CWS), paying less attention to the extensive used various air coolers in industrial practice, including the dry type air cooler (DAC) and wet type air cooler (spray type air cooler (SAC) is a typical example). In addition, the usage of auxiliary pumps in CWS can effectively reduce energy consumption. In this paper, the objective system is a cooler network coupled with the main-auxiliary pump network. In the cooler network, cooling types are considered among CWS, DAC and SAC. Superstructure-based mathematical models are proposed with the minimum total annual cost of the whole system as the objective function. A case study is used to demonstrate the effectiveness of the proposed model, which can obtain the expense reduction with 13% of the cooler network and 32% of the pump network. [ABSTRACT FROM AUTHOR]

We develop a Bayesian approach to computational solution of multistep optimization problems, highlighted in the example of financial portfolio decisions. The approach involves mapping the technical structure of a decision analysis problem to that of Bayesian inference in a purely synthetic "emulating" statistical model. This provides access to standard posterior analytic, simulation and optimization methods that yield indirect solutions of the decision problem. We develop this in time series portfolio analysis using classes of economically and psychologically relevant multi-step ahead portfolio utility functions. Studies with multivariate currency time series illustrate the approach and show some of the practical utility and benefits of the Bayesian emulation methodology. [ABSTRACT FROM AUTHOR]

Reliability-based design optimization (RBDO) aims at making the best decisions when unavoidable efforts of uncertainties are taken into account. To generate an accurate RBDO solution using as small number of function evaluations as possible, a moving shifting vector method is proposed in this work. Reliability index combined with a moving scheme are used to calculate the optimal most probable point and shifting vector, which is much simpler than the original optimal shifting vector method. Moreover, the result of reliability analysis in the proposed method is used to check the effectiveness of probabilistic constraint, which results in a novel constraint handling scheme with the aim of simplifying the deterministic optimization model in MSV. The new MSV method is very efficient for nonlinear problems. Two mathematical problems and a multi-component Tailor-welded blank structure crashworthiness design application are tested to show considerable savings of function evaluations in the proposed methods. [ABSTRACT FROM AUTHOR]

A novel optimization framework for resource allocation in wireless networks and radar systems is proposed, which merges the methods of maximum block improvement (MBI) and of sequential optimization. A detailed convergence proof is provided, showing that the proposed algorithm is able to monotonically increase the objective value while ensuring that every limit point of the generated variable sequence fulfills the problem first-order optimality conditions under very mild hypothesis. These results extend available convergence results on MBI and sequential optimization, significantly widening the range of applications that can be handled by the proposed framework compared to available approaches. This point is illustrated in detail presenting relevant applications from both the cellular and radar context, which fall under the umbrella of the developed optimization method. [ABSTRACT FROM AUTHOR]

The Energy-efficiency of demand management technologies and customer’s experience have emerged as important issues as consumers began to heavily adopt these technologies. In this context, where the electrical load imposed on the smart grid by residential users needs to be optimized, it can be better managed when customer’s comfort parameters are used, such as thermal comfort and preferred appliance usage time interval. In this paper a multi-layer architecture is proposed that uses a multi-objective optimization model at the energy consumption level to take consumer comfort and experience into consideration. The paper shows how our proposed Clustered Sequential Management (CSM) approach could improve consumer comfort via appliance use scheduling. To quantify thermal comfort, we use thermodynamic solutions for a Heating Ventilation and Air Conditioner (HVAC) system and then apply our scheduling model to find the best time slot for such thermal loads, linking consumer experience to power consumption. In addition to thermal loads, we also include non-thermal loads in the cost minimization and the enhanced consumer experience. In this hierarchal algorithm, we classified appliances by their load profile including degrees of freedom for consumer appliance prioritization. Finally, we scheduled consumption within a Time of Use (ToU) pricing model. In this model, we used Mixed Integer Linear Programming (MILP) and Linear Programming (LP) optimization for different categories with different constraints for various loads. We eliminate the customer’s inconvenience on thermal load considering ASHRAE standard, increase the satisfaction on EV optimal chagrining constrained by minimum cost and achieve the preferred usage time for the non-interruptible deferrable loads. The results show that our model is typically able to achieve cost minimization almost equal to 13% and Peak-to-Average Ratios (PAR) reduction with almost 45%.

Sequential stochastic model predictive control (MPC) is a control framework that sequentially employs two optimizers. The high-level optimizer generates a coarse long-term plan based on nominal uncertainty values, while the low-level optimizer refines the short-term plan by considering all possible realizations of these uncertainties. This paper highlights the advantages of the sequential stochastic MPC framework over the traditional multistage MPC approach. It offers faster computation times, improved feasibility of the optimal control problem (OCP), less conservative solutions, and enhanced flexibility in framework tuning. These advantages of sequential stochastic MPC are demonstrated through a case study on flood control in a hydropower station. Additionally, computational efficiency is further boosted with the simplified method which was proposed previously. The results reveal a remarkable performance improvement with an approximately 85 times faster computation time than the standard multistage MPC. These findings establish sequential stochastic MPC framework with the potential for practical implementation. • Proposing sequential stochastic MPC framework. • Uses two optimization steps sequentially to compute an optimal control input. • Demonstrate the better control solution than multistage MPC. • Offers more flexibilities in tuning. • Demonstrated Better feasibility and less computational demand than multistage MPC. [ABSTRACT FROM AUTHOR]