Optimization of ethylene bioproduction in Synechocystis sp. PCC 6803

Ethylene is the most widely produced petrochemical feedstock globally. It is currently produced exclusively from fossil fuels through petroleum fractionation, the largest CO₂-emitting process in the chemical industry. In this study, the efe gene encoding an ethylene-forming enzyme was expressed in Synechocystis sp. PCC 6803, leading to continuous ethylene production. By optimizing concentrations of key nutrients in the media of Synechocystis, we achieved a better understanding of the limiting nutrients that lead to optimal ethylene bioproduction. Using response surface methodology, we determined that major nutrients found in the standard Synechocystis media—NO₃⁺, PO₄³⁻, SO₄²⁻, Ca⁺, Mg⁺, and HCO₃⁻—are required for optimal growth, suggesting that ethylene production is strongly correlated with general growth

Deep Dictionary Learning with An Intra-class Constraint

In recent years, deep dictionary learning (DDL)has attracted a great amount of attention due to its effectiveness for representation learning and visual recognition.~However, most existing methods focus on unsupervised deep dictionary learning, failing to further explore the category information.~To make full use of the category information of different samples, we propose a novel deep dictionary learning model with an intra-class constraint (DDLIC) for visual classification. Specifically, we design the intra-class compactness constraint on the intermediate representation at different levels to encourage the intra-class representations to be closer to each other, and eventually the learned representation becomes more discriminative.~Unlike the traditional DDL methods, during the classification stage, our DDLIC performs a layer-wise greedy optimization in a similar way to the training stage. Experimental results on four image datasets show that our method is superior to the state-of-the-art methods.Comment: 6 pages, 3 figures, 2 tables. It has been accepted in ICME202

Chaos synchronization of the master-slave generalized Lorenz systems via linear state error feedback control

This paper provides a unified method for analyzing chaos synchronization of the generalized Lorenz systems. The considered synchronization scheme consists of identical master and slave generalized Lorenz systems coupled by linear state error variables. A sufficient synchronization criterion for a general linear state error feedback controller is rigorously proven by means of linearization and Lyapunov's direct methods. When a simple linear controller is used in the scheme, some easily implemented algebraic synchronization conditions are derived based on the upper and lower bounds of the master chaotic system. These criteria are further optimized to improve their sharpness. The optimized criteria are then applied to four typical generalized Lorenz systems, i.e. the classical Lorenz system, the Chen system, the Lv system and a unified chaotic system, obtaining precise corresponding synchronization conditions. The advantages of the new criteria are revealed by analytically and numerically comparing their sharpness with that of the known criteria existing in the literature.Comment: 61 pages, 15 figures, 1 tabl