OptDesign: Identifying Optimum Design Strategies in Strain Engineering for Biochemical Production.

Computational tools have been widely adopted for strain optimization in metabolic engineering, contributing to numerous success stories of producing industrially relevant biochemicals. However, most of these tools focus on single metabolic intervention strategies (either gene/reaction knockout or amplification alone) and rely on hypothetical optimality principles (e.g.

Handling Dynamic Multiobjective Optimization Environments via Layered Prediction and Subspace-Based Diversity Maintenance.

In this article, we propose an evolutionary algorithm based on layered prediction (LP) and subspace-based diversity maintenance (SDM) for handling dynamic multiobjective optimization (DMO) environments. The LP strategy takes into account different levels of progress by different individuals in evolution and historical information to predict the population in the event of environmental changes for a prompt change response. The SDM strategy identifies gaps in population distribution and employs a gap-filling technique to increase population diversity.

CaR: A Cutting and Repulsion-Based Evolutionary Framework for Mixed-Integer Programming Problems.

A mixed-integer programming (MIP) problem contains both constraints and integer restrictions. Integer restrictions divide the feasible region defined by constraints into multiple discontinuous feasible parts. In particular, the number of discontinuous feasible parts will drastically increase with the increase of the number of integer decision variables and/or the size of the candidate set of each integer decision variable.

Solving dynamic multi-objective problems with a new prediction-based optimization algorithm.

This paper proposes a new dynamic multi-objective optimization algorithm by integrating a new fitting-based prediction (FBP) mechanism with regularity model-based multi-objective estimation of distribution algorithm (RM-MEDA) for multi-objective optimization in changing environments. The prediction-based reaction mechanism aims to generate high-quality population when changes occur, which includes three subpopulations for tracking the moving Pareto-optimal set effectively. The first subpopulation is created by a simple linear prediction model with two different stepsizes.

Deep Learning Based Prediction on Greenhouse Crop Yield Combined TCN and RNN.

Currently, greenhouses are widely applied for plant growth, and environmental parameters can also be controlled in the modern greenhouse to guarantee the maximum crop yield. In order to optimally control greenhouses' environmental parameters, one indispensable requirement is to accurately predict crop yields based on given environmental parameter settings. In addition, crop yield forecasting in greenhouses plays an important role in greenhouse farming planning and management, which allows cultivators and farmers to utilize the yield prediction results to make knowledgeable management and financial decisions.

Neural modeling of antisaccade performance of healthy controls and early Huntington's disease patients.

Huntington's disease (HD), a genetically determined neurodegenerative disease, is positively correlated with eye movement abnormalities in decision making. The antisaccade conflict paradigm has been widely used to study response inhibition in eye movements, and reliable performance deficits in HD subjects have been observed, including a greater number and timing of direction errors. We recorded the error rates and response latencies of early HD patients and healthy age-matched controls performing the mirror antisaccade task.

A novel and sensitive electrochemical sensor based on nanoporous gold for determination of As(III).

Three-dimensional porous gold nanoparticles (NPG) were synthesized in situ on indium-doped tin oxide (ITO) substrates by a green and convenient one-step electrodeposition method to achieve super-sensitive As(III) detection. The introduction of NPG method not only greatly improves the electron transfer capacity and surface area of sensor interface but provides more active sites for As(III) enrichment, thus boosting sensitivity and selectivity. The sensor was characterized by scanning electron microscopy, energy dispersion spectroscopy, differential pulse anode stripping voltammetry (DPASV), and electrochemical impedance to evaluate its morphology, composition, and electrochemical performance.

NIHBA: a network interdiction approach for metabolic engineering design.

Motivation: Flux balance analysis (FBA) based bilevel optimization has been a great success in redesigning metabolic networks for biochemical overproduction. To date, many computational approaches have been developed to solve the resulting bilevel optimization problems. However, most of them are of limited use due to biased optimality principle, poor scalability with the size of metabolic networks, potential numeric issues or low quantity of design solutions in a single run.

A novel electrochemical sensor for determination of hydroxyl radicals in living cells by coupling nanoporous gold layer with self-assembled 6-(Ferrocenyl) hexanethiol.

The detection of hydroxyl radicals (•OH) in live cells is significant to study its physiological and pathological roles, while it is full of challenge due to the extremely low concentration and short lifetime of •OH. Herein, we have developed a novel electrochemical sensor based on 6-(Ferrocenyl) hexanethiol (6-FcHT) self-assembled nanoporous gold layer (NPGL) modified GE (6-FcHT/NPGL/GE), which can detect the release of •OH from living cells with high sensitivity and selectivity. The superior sensitivity can stem from the unique porous architecture of NPGL, which enlarged electrode surface area and expedited electron transportation during electrochemical reactions.

A Scalable Test Suite for Continuous Dynamic Multiobjective Optimization.

Dynamic multiobjective optimization (DMO) has gained increasing attention in recent years. Test problems are of great importance in order to facilitate the development of advanced algorithms that can handle dynamic environments well. However, many of the existing dynamic multiobjective test problems have not been rigorously constructed and analyzed, which may induce some unexpected bias when they are used for algorithmic analysis.

Evolutionary Dynamic Multiobjective Optimization: Benchmarks and Algorithm Comparisons.

Dynamic multiobjective optimization (DMO) has received growing research interest in recent years since many real-world optimization problems appear to not only have multiple objectives that conflict with each other but also change over time. The time-varying characteristics of these DMO problems (DMOPs) pose new challenges to evolutionary algorithms. Considering the importance of a representative and diverse set of benchmark functions for DMO, in this paper, we propose a new benchmark generator that is able to tune a number of challenging characteristics, including mixed Pareto-optimal front (convexity-concavity), nonmonotonic and time-varying variable-linkages, mixed types of changes, and randomness in type change, which have rarely or not been considered or tested in the literature.

An Improved Multiobjective Optimization Evolutionary Algorithm Based on Decomposition for Complex Pareto Fronts.

The multiobjective evolutionary algorithm based on decomposition (MOEA/D) has been shown to be very efficient in solving multiobjective optimization problems (MOPs). In practice, the Pareto-optimal front (POF) of many MOPs has complex characteristics. For example, the POF may have a long tail and sharp peak and disconnected regions, which significantly degrades the performance of MOEA/D.