Determination of an optimal control strategy for vaccine administration in COVID-19 pandemic treatment

BACKGROUND AND OBJECTIVE

For decades, mathematical models have been used to predict the behavior of physical and biological systems, as well as to define strategies aiming at the minimization of the effects regarding different types of diseases. In the present days, the development of mathematical models to simulate the dynamic behavior of the novel coronavirus disease (COVID-19) is considered an important theme due to the quantity of infected people worldwide. In this work, the objective is to determine an optimal control strategy for vaccine administration in COVID-19 pandemic treatment considering real data from China. Two optimal control problems (mono- and multi-objective) to determine a strategy for vaccine administration in COVID-19 pandemic treatment are proposed. The first consists of minimizing the quantity of infected individuals during the treatment. The second considers minimizing together the quantity of infected individuals and the prescribed vaccine concentration during the treatment.

METHODS

An inverse problem is formulated and solved in order to determine the parameters of the compartmental Susceptible-Infectious-Removed model. The solutions for both optimal control problems proposed are obtained by using Differential Evolution and Multi-objective Optimization Differential Evolution algorithms.

RESULTS

A comparative analysis on the influence related to the inclusion of a control strategy in the population subject to the epidemic is carried out, in terms of the compartmental model and its control parameters. The results regarding the proposed optimal control problems provide information from which an optimal strategy for vaccine administration can be defined.

CONCLUSIONS

The solution of the optimal control problem can provide information about the effect of vaccination of a population in the face of an epidemic, as well as essential elements for decision making in the economic and governmental spheres.

Optimal Design of Energy Systems Using Constrained Grey-Box Multi-Objective Optimization

The (global) optimization of energy systems, commonly characterized by high-fidelity and large-scale complex models, poses a formidable challenge partially due to the high noise and/or computational expense associated with the calculation of derivatives. This complexity is further amplified in the presence of multiple conflicting objectives, for which the goal is to generate trade-off compromise solutions, commonly known as Pareto-optimal solutions. We have previously introduced the p-ARGONAUT system, parallel AlgoRithms for Global Optimization of coNstrAined grey-box compUTational problems, which is designed to optimize general constrained single objective grey-box problems by postulating accurate and tractable surrogate formulations for all unknown equations in a computationally efficient manner. In this work, we extend p-ARGONAUT towards multi-objective optimization problems and test the performance of the framework, both in terms of accuracy and consistency, under many equality constraints. Computational results are reported for a number of benchmark multi-objective problems and a case study of an energy market design problem for a commercial building, while the performance of the framework is compared with other derivative-free optimization solvers.

Optimal allocation of physical water resources integrated with virtual water trade in water scarce regions: A case study for Beijing, China

This study provides an innovative application of virtual water trade in the traditional allocation of physical water resources in water scarce regions. A multi-objective optimization model was developed to optimize the allocation of physical water and virtual water resources to different water users in Beijing, China, considering the trade-offs between economic benefit and environmental impacts of water consumption. Surface water, groundwater, transferred water and reclaimed water constituted the physical resource of water supply side, while virtual water flow associated with the trade of five major crops (barley, corn, rice, soy and wheat) and three livestock products (beef, pork and poultry) in agricultural sector (calculated by the trade quantities of products and their virtual water contents). Urban (daily activities and public facilities), industry, environment and agriculture (products growing) were considered in water demand side. As for the traditional allocation of physical water resources, the results showed that agriculture and urban were the two predominant water users (accounting 54% and 28%, respectively), while groundwater and surface water satisfied around 70% water demands of different users (accounting 36% and 34%, respectively). When considered the virtual water trade of eight agricultural products in water allocation procedure, the proportion of agricultural consumption decreased to 45% in total water demand, while the groundwater consumption decreased to 24% in total water supply. Virtual water trade overturned the traditional components of water supplied from different sources for agricultural consumption, and became the largest water source in Beijing. Additionally, it was also found that environmental demand took a similar percentage of water consumption in each water source. Reclaimed water was the main water source for industrial and environmental users. The results suggest that physical water resources would mainly satisfy the consumption of urban and environment, and the unbalance between water supply and demand could be filled by virtual water import in water scarce regions.

A Food-Energy-Water Nexus approach for land use optimization

Allocation and management of agricultural land is of emergent concern due to land scarcity, diminishing supply of energy and water, and the increasing demand of food globally. To achieve social, economic and environmental goals in a specific agricultural land area, people and society must make decisions subject to the demand and supply of food, energy and water (FEW). Interdependence among these three elements, the Food-Energy-Water Nexus (FEW-N), requires that they be addressed concertedly. Despite global efforts on data, models and techniques, studies navigating the multi-faceted FEW-N space, identifying opportunities for synergistic benefits, and exploring interactions and trade-offs in agricultural land use system are still limited. Taking an experimental station in China as a model system, we present the foundations of a systematic engineering framework and quantitative decision-making tools for the trade-off analysis and optimization of stressed interconnected FEW-N networks. The framework combines data analytics and mixed-integer nonlinear modeling and optimization methods establishing the interdependencies and potentially competing interests among the FEW elements in the system, along with policy, sustainability, and feedback from various stakeholders. A multi-objective optimization strategy is followed for the trade-off analysis empowered by the introduction of composite FEW-N metrics as means to facilitate decision-making and compare alternative process and technological options. We found the framework works effectively to balance multiple objectives and benchmark the competitions for systematic decisions. The optimal solutions tend to promote the food production with reduced consumption of water and energy, and have a robust performance with alternative pathways under different climate scenarios.

Modeling municipal solid waste collection: A generalized vehicle routing model with multiple transfer stations, gather sites and inhomogeneous vehicles in time windows

Municipal Solid Waste (MSW) collection is a necessary process in any municipality resulting in the quality-of-life, economic aspects and urban structuralization. The intrinsic nature of MSW collection relates to the development of effective vehicle routing models that optimize the total traveling distances of vehicles, the environmental emission and the investment costs. In this article, we propose a generalized vehicle routing model including multiple transfer stations, gather sites and inhomogeneous vehicles in time windows for MSW collection. It takes into account traveling in one-way routes, the number of vehicles per m(2) and waiting time at traffic stops for reduction of operational time. The proposed model could be used for scenarios having similar node structures and vehicles' characteristics. A case study at Danang city, Vietnam is given to illustrate the applicability of this model. The experimental results have clearly shown that the new model reduces both total traveling distances and operational hours of vehicles in comparison with those of practical scenarios. Optimal routes of vehicles on streets and markets at Danang are given. Those results are significant to practitioners and local policy makers.

Spatially dynamic forest management to sustain biodiversity and economic returns

Production of marketed commodities and protection of biodiversity in natural systems often conflict and thus the continuously expanding human needs for more goods and benefits from global ecosystems urgently calls for strategies to resolve this conflict. In this paper, we addressed what is the potential of a forest landscape to simultaneously produce habitats for species and economic returns, and how the conflict between habitat availability and timber production varies among taxa. Secondly, we aimed at revealing an optimal combination of management regimes that maximizes habitat availability for given levels of economic returns. We used multi-objective optimization tools to analyze data from a boreal forest landscape consisting of about 30,000 forest stands simulated 50 years into future. We included seven alternative management regimes, spanning from the recommended intensive forest management regime to complete set-aside of stands (protection), and ten different taxa representing a wide variety of habitat associations and social values. Our results demonstrate it is possible to achieve large improvements in habitat availability with little loss in economic returns. In general, providing dead-wood associated species with more habitats tended to be more expensive than providing requirements for other species. No management regime alone maximized habitat availability for the species, and systematic use of any single management regime resulted in considerable reductions in economic returns. Compared with an optimal combination of management regimes, a consistent application of the recommended management regime would result in 5% reduction in economic returns and up to 270% reduction in habitat availability. Thus, for all taxa a combination of management regimes was required to achieve the optimum. Refraining from silvicultural thinnings on a proportion of stands should be considered as a cost-effective management in commercial forests to reconcile the conflict between economic returns and habitat required by species associated with dead-wood. In general, a viable strategy to maintain biodiversity in production landscapes would be to diversify management regimes. Our results emphasize the importance of careful landscape level forest management planning because optimal combinations of management regimes were taxon-specific. For cost-efficiency, the results call for balanced and correctly targeted strategies among habitat types.

DrugEx v2: de novo design of drug molecules by Pareto-based multi-objective reinforcement learning in polypharmacology

In polypharmacology drugs are required to bind to multiple specific targets, for example to enhance efficacy or to reduce resistance formation. Although deep learning has achieved a breakthrough in de novo design in drug discovery, most of its applications only focus on a single drug target to generate drug-like active molecules. However, in reality drug molecules often interact with more than one target which can have desired (polypharmacology) or undesired (toxicity) effects. In a previous study we proposed a new method named DrugEx that integrates an exploration strategy into RNN-based reinforcement learning to improve the diversity of the generated molecules. Here, we extended our DrugEx algorithm with multi-objective optimization to generate drug-like molecules towards multiple targets or one specific target while avoiding off-targets (the two adenosine receptors, A₁AR and A_2AAR, and the potassium ion channel hERG in this study). In our model, we applied an RNN as the agent and machine learning predictors as the environment. Both the agent and the environment were pre-trained in advance and then interplayed under a reinforcement learning framework. The concept of evolutionary algorithms was merged into our method such that crossover and mutation operations were implemented by the same deep learning model as the agent. During the training loop, the agent generates a batch of SMILES-based molecules. Subsequently scores for all objectives provided by the environment are used to construct Pareto ranks of the generated molecules. For this ranking a non-dominated sorting algorithm and a Tanimoto-based crowding distance algorithm using chemical fingerprints are applied. Here, we adopted GPU acceleration to speed up the process of Pareto optimization. The final reward of each molecule is calculated based on the Pareto ranking with the ranking selection algorithm. The agent is trained under the guidance of the reward to make sure it can generate desired molecules after convergence of the training process. All in all we demonstrate generation of compounds with a diverse predicted selectivity profile towards multiple targets, offering the potential of high efficacy and low toxicity.

Exploring synergistic benefits of Water-Food-Energy Nexus through multi-objective reservoir optimization schemes

This study proposed a holistic three-fold scheme that synergistically optimizes the benefits of the Water-Food-Energy (WFE) Nexus by integrating the short/long-term joint operation of a multi-objective reservoir with irrigation ponds in response to urbanization. The three-fold scheme was implemented step by step: (1) optimizing short-term (daily scale) reservoir operation for maximizing hydropower output and final reservoir storage during typhoon seasons; (2) simulating long-term (ten-day scale) water shortage rates in consideration of the availability of irrigation ponds for both agricultural and public sectors during non-typhoon seasons; and (3) promoting the synergistic benefits of the WFE Nexus in a year-round perspective by integrating the short-term optimization and long-term simulation of reservoir operations. The pivotal Shihmen Reservoir and 745 irrigation ponds located in Taoyuan City of Taiwan together with the surrounding urban areas formed the study case. The results indicated that the optimal short-term reservoir operation obtained from the non-dominated sorting genetic algorithm II (NSGA-II) could largely increase hydropower output but just slightly affected water supply. The simulation results of the reservoir coupled with irrigation ponds indicated that such joint operation could significantly reduce agricultural and public water shortage rates by 22.2% and 23.7% in average, respectively, as compared to those of reservoir operation excluding irrigation ponds. The results of year-round short/long-term joint operation showed that water shortage rates could be reduced by 10% at most, the food production rate could be increased by up to 47%, and the hydropower benefit could increase up to 9.33 million USD per year, respectively, in a wet year. Consequently, the proposed methodology could be a viable approach to promoting the synergistic benefits of the WFE Nexus, and the results provided unique insights for stakeholders and policymakers to pursue sustainable urban development plans.

An analytical study of modified multi-objective Harris Hawk Optimizer towards medical data feature selection

Dimensionality reduction or Feature Selection (FS) is a multi-target optimization problem with two goals: improving the classification efficiency while simultaneously dropping the characteristics. Harris Hawk Optimization (HHO) is introduced recently to solve different demanding optimization tasks as a metaheuristic tool. The initial HHO is for addressing optimization problems in a continuous environment, but FS is an optimization task in binary space. Therefore, in this article, a Multi-Objective Quadratic Binary HHO (MOQBHHO) technique with K-Nearest Neighbor (KNN) method as wrapper classifier is implemented for extracting the optimal feature subsets. Finally, this study uses the crowding distance (CD) value as a third criterion for picking the best one from the non-dominated solutions. Here, to estimate the performance of the proposed approach, twelve standard medical datasets are considered. The proposed MOQBHHO is compared with MOBHHO-S (using a sigmoid function), multi-objective genetic algorithm (MOGA), multi-objective ant lion optimization (MOALO), and NSGA-II. The experimental findings show that the proposed MOQBHHO finds a set of non-dominated feature subsets effectively in contrast to deep-based FS methods: Auto-encoder and Teacher-Student based FS (TSFS). The presented methodology is found superior in obtaining the best trade-off between two fitness assessment criteria compared to the other existing multi-objective techniques for recognizing relevant features.

Multi-objective optimization of the cavitation generation unit structure of an advanced rotational hydrodynamic cavitation reactor

Hydrodynamic cavitation (HC) has been widely considered a promising technique for industrial-scale process intensifications. The effectiveness of HC is determined by the performance of hydrodynamic cavitation reactors (HCRs). The advanced rotational HCRs (ARHCRs) proposed recently have shown superior performance in various applications, while the research on the structural optimization is still absent. The present study, for the first time, identifies optimal structures of the cavitation generation units of a representative ARHCR by combining genetic algorithm (GA) and computational fluid dynamics, with the objectives of maximizing the total vapor volume, V_vapor , and minimizing the total torque of the rotor wall, M→_z . Four important geometrical factors, namely, diameter (D), interaction distance (s), height (h), and inclination angle (θ), were specified as the design variables. Two high-performance fitness functions for V_vapor and M→_z were established from a central composite design with 25 cases. After performing 10,001 simulations of GA, a Pareto front with 1630 non-dominated points was obtained. The results reveal that the values of s and θ of the Pareto front concentrated on their lower (i.e., 1.5 mm) and upper limits (i.e., 18.75°), respectively, while the values of D and h were scattered in their variation regions. In comparison to the original model, a representative global optimal point increased the V_vapor by 156% and decreased the M→_z by 14%. The corresponding improved mechanism was revealed by analyzing the flow field. The findings of this work can strongly support the fundamental understanding, design, and application of ARHCRs for process intensifications.

DrugEx v3: scaffold-constrained drug design with graph transformer-based reinforcement learning

Rational drug design often starts from specific scaffolds to which side chains/substituents are added or modified due to the large drug-like chemical space available to search for novel drug-like molecules. With the rapid growth of deep learning in drug discovery, a variety of effective approaches have been developed for de novo drug design. In previous work we proposed a method named DrugEx, which can be applied in polypharmacology based on multi-objective deep reinforcement learning. However, the previous version is trained under fixed objectives and does not allow users to input any prior information (i.e. a desired scaffold). In order to improve the general applicability, we updated DrugEx to design drug molecules based on scaffolds which consist of multiple fragments provided by users. Here, a  Transformer model was employed to generate molecular structures. The Transformer is a multi-head self-attention deep learning model containing an encoder to receive scaffolds as input and a decoder to generate molecules as output. In order to deal with the graph representation of molecules a novel positional encoding for each atom and bond based on an adjacency matrix was proposed, extending the architecture of the Transformer. The graph Transformer model contains growing and connecting procedures for molecule generation starting from  a given scaffold based on fragments. Moreover, the generator was trained under a reinforcement learning framework to increase the number of desired ligands. As a proof of concept, the method was applied to design ligands for the adenosine A_2A receptor (A_2AAR) and compared with SMILES-based methods. The results show that 100% of the generated molecules are valid and most of them had a high predicted affinity value towards A_2AAR with given scaffolds.

Optimization of nonlinear quarter car suspension-seat-driver model

In this paper a nonlinear quarter car suspension–seat–driver model was implemented for optimum design. A nonlinear quarter car model comprising of quadratic tyre stiffness and cubic stiffness in suspension spring, frame, and seat cushion with 4 degrees of freedom (DoF) driver model was presented for optimization and analysis. Suspension system was aimed to optimize the comfort and health criterion comprising of Vibration Dose Value (VDV) at head, frequency weighted RMS head acceleration, crest factor, amplitude ratio of head RMS acceleration to seat RMS acceleration and amplitude ratio of upper torso RMS acceleration to seat RMS acceleration along with stability criterion comprising of suspension space deflection and dynamic tyre force. ISO 2631-1 standard was adopted to assess ride and health criterions. Suspension spring stiffness and damping and seat cushion stiffness and damping are the design variables. Non-dominated Sort Genetic Algorithm (NSGA-II) and Multi-Objective Particle Swarm Optimization – Crowding Distance (MOPSO-CD) algorithm are implemented for optimization. Simulation result shows that optimum design improves ride comfort and health criterion over classical design variables.

A three-dimensional micromechanical model of brain white matter with histology-informed probabilistic distribution of axonal fibers

This paper presents a three-dimensional micromechanical model of brain white matter tissue as a transversely isotropic soft composite described by the generalized Ogden hyperelastic model. The embedded element technique, with corrected stiffness redundancy in large deformations, was used for the embedment of a histology-informed probabilistic distribution of the axonal fibers in the extracellular matrix. The model was linked to a multi-objective, multi-parametric optimization algorithm, using the response surface methodology, for characterization of material properties of the axonal fibers and extracellular matrix in an inverse finite element analysis. The optimum hyperelastic characteristics of the tissue constituents, obtained based on the axonal and transverse direction test results of the corona radiata tissue samples, indicated that the axonal fibers were almost thirteen times stiffer than the extracellular matrix under large deformations. Simulation of the same tissue under a different loading condition, as well as that of another white matter tissue, i.e., the corpus callosum, in the axonal and transverse directions, using the optimized hyperelastic characteristics revealed tissue responses very close to those of the experiments. The results of the model at the sub-tissue level indicated that the stress concentrations were considerably large around the small axons, which might contribute into the brain injury.

Optimal drug regimens in cancer chemotherapy: a multi-objective approach

In this paper, based on two mathematical models, a set of optimal anticancer drug regimens is designed. Two important difficulties in treating cancers with chemotherapy are drug resistance and toxicity. They were considered in our treatment design; a compartmental model was extended to appropriately describe drug resistance and two constraints were imposed on the value of the anticancer drug dynamics to avoid toxicity. Indeed, we optimized two objective functions simultaneously in order to minimize the size of the tumor as well as the side effects of the anticancer drug on the patient's body. Operating the described task, a bi-objective optimization problem was defined and solved by an evolutionary method. This optimization led to a set of optimal drug regimens, which can be used in several chemotherapy treatments. Numerical simulations were given to illustrate the flexibility of the method.

Metabolic engineering with multi-objective optimization of kinetic models

Kinetic models have a great potential for metabolic engineering applications. They can be used for testing which genetic and regulatory modifications can increase the production of metabolites of interest, while simultaneously monitoring other key functions of the host organism. This work presents a methodology for increasing productivity in biotechnological processes exploiting dynamic models. It uses multi-objective dynamic optimization to identify the combination of targets (enzymatic modifications) and the degree of up- or down-regulation that must be performed in order to optimize a set of pre-defined performance metrics subject to process constraints. The capabilities of the approach are demonstrated on a realistic and computationally challenging application: a large-scale metabolic model of Chinese Hamster Ovary cells (CHO), which are used for antibody production in a fed-batch process. The proposed methodology manages to provide a sustained and robust growth in CHO cells, increasing productivity while simultaneously increasing biomass production, product titer, and keeping the concentrations of lactate and ammonia at low values. The approach presented here can be used for optimizing metabolic models by finding the best combination of targets and their optimal level of up/down-regulation. Furthermore, it can accommodate additional trade-offs and constraints with great flexibility.

Multi-objective optimization framework to obtain model-based guidelines for tuning biological synthetic devices: an adaptive network case

Background

Model based design plays a fundamental role in synthetic biology. Exploiting modularity, i.e. using biological parts and interconnecting them to build new and more complex biological circuits is one of the key issues. In this context, mathematical models have been used to generate predictions of the behavior of the designed device. Designers not only want the ability to predict the circuit behavior once all its components have been determined, but also to help on the design and selection of its biological parts, i.e. to provide guidelines for the experimental implementation. This is tantamount to obtaining proper values of the model parameters, for the circuit behavior results from the interplay between model structure and parameters tuning. However, determining crisp values for parameters of the involved parts is not a realistic approach. Uncertainty is ubiquitous to biology, and the characterization of biological parts is not exempt from it. Moreover, the desired dynamical behavior for the designed circuit usually results from a trade-off among several goals to be optimized.

Results

We propose the use of a multi-objective optimization tuning framework to get a model-based set of guidelines for the selection of the kinetic parameters required to build a biological device with desired behavior. The design criteria are encoded in the formulation of the objectives and optimization problem itself. As a result, on the one hand the designer obtains qualitative regions/intervals of values of the circuit parameters giving rise to the predefined circuit behavior; on the other hand, he obtains useful information for its guidance in the implementation process. These parameters are chosen so that they can effectively be tuned at the wet-lab, i.e. they are effective biological tuning knobs. To show the proposed approach, the methodology is applied to the design of a well known biological circuit: a genetic incoherent feed-forward circuit showing adaptive behavior.

Conclusion

The proposed multi-objective optimization design framework is able to provide effective guidelines to tune biological parameters so as to achieve a desired circuit behavior. Moreover, it is easy to analyze the impact of the context on the synthetic device to be designed. That is, one can analyze how the presence of a downstream load influences the performance of the designed circuit, and take it into account.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-016-0269-0) contains supplementary material, which is available to authorized users.

Bi-objective optimization for a multi-period COVID-19 vaccination planning problem

This work investigates a new multi-period vaccination planning problem that simultaneously optimizes the total travel distance of vaccination recipients (service level) and the operational cost. An optimal plan determines, for each period, which vaccination sites to open, how many vaccination stations to launch at each site, how to assign recipients from different locations to opened sites, and the replenishment quantity of each site. We formulate this new problem as a bi-objective mixed-integer linear program (MILP). We first propose a weighted-sum and an ϵ -constraint methods, which rely on solving many single-objective MILPs and thus lose efficiency for practical-sized instances. To this end, we further develop a tailored genetic algorithm where an improved assignment strategy and a new dynamic programming method are designed to obtain good feasible solutions. Results from a case study indicate that our methods reduce the operational cost and the total travel distance by up to 9.3% and 36.6%, respectively. Managerial implications suggest enlarging the service capacity of vaccination sites can help improve the performance of the vaccination program. The enhanced performance of our heuristic is due to the newly proposed assignment strategy and dynamic programming method. Our findings demonstrate that vaccination programs during pandemics can significantly benefit from formal methods, drastically improving service levels and decreasing operational costs.

Multi-objective optimization of solid/liquid extraction of total sunflower proteins from cold press meal

The impact of pH (6-9) and NaCl concentration (0-0.5 mol.L^-1) on sunflower protein extraction was studied through design of experiments. The considered criteria were protein extraction yield (total proteins, helianthinin and albumins), chlorogenic acids covalently bound to proteins, and free chlorogenic acid concentration in the aqueous extract. Statistical analysis showed that the obtained by design of experiments the polynomial models of each extraction criteria were reliable for predicting the responses. They were employed in an original multi-objective optimization methodology. The optimal conditions revealed to be pH 7.3/0.3 mol.L^-1 NaCl yielded 46.83% and 59.16% of total protein and albumin extraction yield, 1.730 and 1.998 mg.g^-1 of chlorogenic acids covalently bound to helianthinin and albumins in aqueous extract, respectively. The sunflower protein isolate obtained after extraction in this condition had good solubility (40-80% at pH 5-8), functional properties (foaming and emulsifying) and a satisfying color.

Concurrent production of cellulase and xylanase from Trichoderma reesei NCIM 1186: enhancement of production by desirability-based multi-objective method

Application of multiple response optimizations using desirability function in the production of microbial metabolites improves economy and efficiency. Concurrent production of cellulase and xylanase in Trichoderma reesei NCIM 1186 using an agricultural weed, Prosopis juliflora pods, was studied. The main aim of the study was to optimize significant medium nutrient parameters for maximization of cellulase and xylanase by multi-objective optimization strategy using biomass. Process parameters such as the nutrient concentrations (pods, sucrose, and yeast extract) and pH were investigated to improve cellulase and xylanase activities by one factor at a time approach, single response optimization and multi-objective optimization. At the corresponding optimized process parameters in single response optimization, the maximum cellulase activity observed was 3055.65 U/L where xylanase highest activity was 422.16 U/L. Similarly, the maximum xylanase activity, 444.94 U/L, was observed with the highest cellulase activity of 2804.40 U/L. The multi-objective optimization finds a tradeoff between the two objectives and optimal activity values in between the single-objective optima were achieved, 3033.74 and 439.13 U/L for cellulase and xylanase, respectively.

Multi-step ahead meningitis case forecasting based on decomposition and multi-objective optimization methods

Epidemiological time series forecasting plays an important role in health public systems, due to its ability to allow managers to develop strategic planning to avoid possible epidemics. In this paper, a hybrid learning framework is developed to forecast multi-step-ahead (one, two, and three-month-ahead) meningitis cases in four states of Brazil. First, the proposed approach applies an ensemble empirical mode decomposition (EEMD) to decompose the data into intrinsic mode functions and residual components. Then, each component is used as the input of five different forecasting models, and, from there, forecasted results are obtained. Finally, all combinations of models and components are developed, and for each case, the forecasted results are weighted integrated (WI) to formulate a heterogeneous ensemble forecaster for the monthly meningitis cases. In the final stage, a multi-objective optimization (MOO) using the Non-Dominated Sorting Genetic Algorithm – version II is employed to find a set of candidates’ weights, and then the Technique for Order of Preference by similarity to Ideal Solution (TOPSIS) is applied to choose the adequate set of weights. Next, the most adequate model is the one with the best generalization capacity out-of-sample in terms of performance criteria including mean absolute error (MAE), relative root mean squared error (RRMSE), and symmetric mean absolute percentage error (sMAPE). By using MOO, the intention is to enhance the performance of the forecasting models by improving simultaneously their accuracy and stability measures. To access the model’s performance, comparisons based on metrics are conducted with: (i) EEMD, heterogeneous ensemble integrated by direct strategy, or simple sum; (ii) EEMD, homogeneous ensemble of components WI; (iii) models without signal decomposition. At this stage, MAE, RRMSE, and sMAPE criteria as well as Diebold–Mariano statistical test are adopted. In all twelve scenarios, the proposed framework was able to perform more accurate and stable forecasts, which showed, on 89.17% of the cases, that the errors of the proposed approach are statistically lower than other approaches. These results showed that combining EEMD, heterogeneous ensemble and WI with weights obtained by optimization can develop precise and stable forecasts. The modeling developed in this paper is promising and can be used by managers to support decision making.

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Ensemble empirical mode decomposition is applied into the raw time series.

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Heterogeneous ensemble learning models are used to forecasting meningitis cases.

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The NSGA-II algorithm and TOPSIS criterion are employed in the multi-objective procedure.

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Proposed model has errors statistically lower than 89.17% of the compared models.

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Promising results are achieved by the weighted integrated ensemble learning model.

Design of homo-organic acid producing strains using multi-objective optimization

Production of homo-organic acids without byproducts is an important challenge in bioprocess engineering to minimize operation cost for separation processes. In this study, we used multi-objective optimization to design Escherichia coli strains with the goals of maximally producing target organic acids, while maintaining sufficiently high growth rate and minimizing the secretion of undesired byproducts. Homo-productions of acetic, lactic and succinic acids were targeted as examples. Engineered E. coli strains capable of producing homo-acetic and homo-lactic acids could be developed by taking this systems approach for the minimal identification of gene knockout targets. Also, failure to predict effective gene knockout targets for the homo-succinic acid production suggests that the multi-objective optimization is useful in assessing the suitability of a microorganism as a host strain for the production of a homo-organic acid. The systems metabolic engineering-based approach reported here should be applicable to the production of other industrially important organic acids.

C-HMOSHSSA: Gene selection for cancer classification using multi-objective meta-heuristic and machine learning methods

BACKGROUND AND OBJECTIVE

Over the last two decades, DNA microarray technology has emerged as a powerful tool for early cancer detection and prevention. It helps to provide a detailed overview of disease complex microenvironment. Moreover, online availability of thousands of gene expression assays made microarray data classification an active research area. A common goal is to find a minimum subset of genes and maximizing the classification accuracy.

METHODS

In pursuit of a similar objective, we have proposed framework (C-HMOSHSSA) for gene selection using multi-objective spotted hyena optimizer (MOSHO) and salp swarm algorithm (SSA). The real-life optimization problems with more than one objective usually face the challenge to maintain convergence and diversity. Salp Swarm Algorithm (SSA) maintains diversity but, suffers from the overhead of maintaining the necessary information. On the other hand, the calculation of MOSHO requires low computational efforts hence is used for maintaining the necessary information. Therefore, the proposed algorithm is a hybrid algorithm that utilizes the features of both SSA and MOSHO to facilitate its exploration and exploitation capability.

RESULTS

Four different classifiers are trained on seven high-dimensional datasets using a subset of features (genes), which are obtained after applying the proposed hybrid gene selection algorithm. The results show that the proposed technique significantly outperforms existing state-of-the-art techniques.

CONCLUSION

It is also shown that the new sets of informative and biologically relevant genes are successfully identified by the proposed technique. The proposed approach can also be applied to other problem domains of interest which involve feature selection.

Optimal control towards sustainable wastewater treatment plants based on multi-agent reinforcement learning

Wastewater treatment plants (WWTPs) are designed to eliminate pollutants and alleviate environmental pollution resulting from human activities. However, the construction and operation of WWTPs consume resources, emit greenhouse gases (GHGs) and produce residual sludge, thus require further optimization. WWTPs are complex to control and optimize because of high non-linearity and variation. This study used a novel technique, multi-agent deep reinforcement learning (MADRL), to simultaneously optimize dissolved oxygen (DO) and chemical dosage in a WWTP. The reward function was specially designed from life cycle perspective to achieve sustainable optimization. Five scenarios were considered: baseline, three different effluent quality and cost-oriented scenarios. The result shows that optimization based on LCA has lower environmental impacts compared to baseline scenario, as cost, energy consumption and greenhouse gas emissions reduce to 0.890 CNY/m³-ww, 0.530 kWh/m³-ww, 2.491 kg CO²-eq/m³-ww respectively. The cost-oriented control strategy exhibits comparable overall performance to the LCA-driven strategy since it sacrifices environmental benefits but has lower cost as 0.873 CNY/m³-ww. It is worth mentioning that the retrofitting of WWTPs based on resources should be implemented with the consideration of impact transfer. Specifically, LCA-SW scenario decreases 10 kg PO⁴-eq in eutrophication potential compared to the baseline within 10 days, while significantly increases other indicators. The major contributors of each indicator are identified for future study and improvement. Last, the authors discussed that novel dynamic control strategies required advanced sensors or a large amount of data, so the selection of control strategies should also consider economic and ecological conditions. In a nutshell, there are still limitations of this work and future studies are required.

Paroxysmal atrial fibrillation prediction based on HRV analysis and non-dominated sorting genetic algorithm III

This paper presents a method that able to predict the paroxysmal atrial fibrillation (PAF). The method uses shorter heart rate variability (HRV) signals when compared to existing methods, and achieves good prediction accuracy. PAF is a common cardiac arrhythmia that increases the health risk of a patient, and the development of an accurate predictor of the onset of PAF is clinical important because it increases the possibility to electrically stabilize and prevent the onset of atrial arrhythmias with different pacing techniques. We propose a multi-objective optimization algorithm based on the non-dominated sorting genetic algorithm III for optimizing the baseline PAF prediction system, that consists of the stages of pre-processing, HRV feature extraction, and support vector machine (SVM) model. The pre-processing stage comprises of heart rate correction, interpolation, and signal detrending. After that, time-domain, frequency-domain, non-linear HRV features are extracted from the pre-processed data in feature extraction stage. Then, these features are used as input to the SVM for predicting the PAF event. The proposed optimization algorithm is used to optimize the parameters and settings of various HRV feature extraction algorithms, select the best feature subsets, and tune the SVM parameters simultaneously for maximum prediction performance. The proposed method achieves an accuracy rate of 87.7%, which significantly outperforms most of the previous works. This accuracy rate is achieved even with the HRV signal length being reduced from the typical 30 min to just 5 min (a reduction of 83%). Furthermore, another significant result is the sensitivity rate, which is considered more important that other performance metrics in this paper, can be improved with the trade-off of lower specificity.

Green Closed-Loop Supply Chain Network Design During the Coronavirus (COVID-19) Pandemic: a Case Study in the Iranian Automotive Industry

This paper presents a new mathematical model of the green closed-loop supply chain network (GCLSCN) during the COVID-19 pandemic. The suggested model can explain the trade-offs between environmental (minimizing CO2 emissions) and economic (minimizing total costs) aspects during the COVID-19 outbreak. Considering the guidelines for hygiene during the outbreak helps us design a new sustainable hygiene supply chain (SC). This model is sensitive to the cost structure. The cost includes two parts: the normal cost without considering the coronavirus pandemic and the cost with considering coronavirus. The economic novelty aspect of this paper is the hygiene costs. It includes disinfection and sanitizer costs, personal protective equipment (PPE) costs, COVID-19 tests, education, medicines, vaccines, and vaccination costs. This paper presents a multi-objective mixed-integer programming (MOMIP) problem for designing a GCLSCN during the pandemic. The optimization procedure uses the scalarization approach, namely the weighted sum method (WSM). The computational optimization process is conducted through Lingo software. Due to the recency of the COVID-19 pandemic, there are still many research gaps. Our contributions to this research are as follows: (i) designed a model of the green supply chain (GSC) and showed the better trade-offs between economic and environmental aspects during the COVID-19 pandemic and lockdowns, (ii) designed the hygiene supply chain, (iii) proposed the new indicators of economic aspects during the COVID-19 outbreak, and (iv) have found the positive (reducing CO2 emissions) and negative (increase in costs) impacts of COVID-19 and lockdowns. Therefore, this study designed a new hygiene model to fill this gap for the COVID-19 condition disaster. The findings of the proposed network illustrate the SC has become greener during the COVID-19 pandemic. The total cost of the network was increased during the COVID-19 pandemic, but the lockdowns had direct positive effects on emissions and air quality.