Genome replication engineering assisted continuous evolution (GREACE) to improve microbial tolerance for biofuels production

BACKGROUND: Microbial production of biofuels requires robust cell growth and metabolism under tough conditions. Conventionally, such tolerance phenotypes were engineered through evolutionary engineering using the principle of “Mutagenesis followed-by Selection”. The iterative rounds of mutagenesis-selection and frequent manual interventions resulted in discontinuous and inefficient strain improvement processes. This work aimed to develop a more continuous and efficient evolutionary engineering method termed as “Genome Replication Engineering Assisted Continuous Evolution” (GREACE) using “Mutagenesis coupled-with Selection” as its core principle. RESULTS: The core design of GREACE is to introduce an in vivo continuous mutagenesis mechanism into microbial cells by introducing a group of genetically modified proofreading elements of the DNA polymerase complex to accelerate the evolution process under stressful conditions. The genotype stability and phenotype heritability can be stably maintained once the genetically modified proofreading element is removed, thus scarless mutants with desired phenotypes can be obtained. Kanamycin resistance of E. coli was rapidly improved to confirm the concept and feasibility of GREACE. Intrinsic mechanism analysis revealed that during the continuous evolution process, the accumulation of genetically modified proofreading elements with mutator activities endowed the host cells with enhanced adaptation advantages. We further showed that GREACE can also be applied to engineer n-butanol and acetate tolerances. In less than a month, an E. coli strain capable of growing under an n-butanol concentration of 1.25% was isolated. As for acetate tolerance, cell growth of the evolved E. coli strain increased by 8-fold under 0.1% of acetate. In addition, we discovered that adaptation to specific stresses prefers accumulation of genetically modified elements with specific mutator strengths. CONCLUSIONS: We developed a novel GREACE method using “Mutagenesis coupled-with Selection” as core principle. Successful isolation of E. coli strains with improved n-butanol and acetate tolerances demonstrated the potential of GREACE as a promising method for strain improvement in biofuels production

Cooperative Control of Regenerative Braking and Antilock Braking for a Hybrid Electric Vehicle

A new cooperative braking control strategy (CBCS) is proposed for a parallel hybrid electric vehicle (HEV) with both a regenerative braking system and an antilock braking system (ABS) to achieve improved braking performance and energy regeneration. The braking system of the vehicle is based on a new method of HEV braking torque distribution that makes the antilock braking system work together with the regenerative braking system harmoniously. In the cooperative braking control strategy, a sliding mode controller (SMC) for ABS is designed to maintain the wheel slip within an optimal range by adjusting the hydraulic braking torque continuously; to reduce the chattering in SMC, a boundary-layer method with moderate tuning of a saturation function is also investigated; based on the wheel slip ratio, battery state of charge (SOC), and the motor speed, a fuzzy logic control strategy (FLC) is applied to adjust the regenerative braking torque dynamically. In order to evaluate the performance of the cooperative braking control strategy, the braking system model of a hybrid electric vehicle is built in MATLAB/SIMULINK. It is found from the simulation that the cooperative braking control strategy suggested in this paper provides satisfactory braking performance, passenger comfort, and high regenerative efficiency

Bis{1-[4-(benz­yloxy)phen­yl]-4,4,4-tri­fluoro­butane-1,3-dionato(1−)}dipyri­dine­cobalt(II)

In the title compound, [Co(C17H12F3O3)2(C5H5N)2], the CoII ion is situated on a twofold rotation axis, coordinated by four O atoms from two 1-[4-(benz­yloxy)phen­yl]-4,4,4-trifluoro­butane-1,3-dionate(1−) (L) ligands and two N atoms from two pyridine ligands in a distorted octa­hedral geometry. The two pyridine rings form a dihedral angle of 84.63 (7)°. The two benzene rings in L are twisted at 58.83 (5)°. Weak inter­molecular C—H⋯F hydrogen bonds consolidate the crystal packing

Rear-end collision escape algorithm for intelligent vehicles supported by vehicular communication

To reduce rear-end collision risks and improve traffic safety, a novel rear-end collision escape algorithm is proposed for intelligent vehicles supported by vehicular communication. Numerous research has been carried out on rear-end collision avoidance. Most of these studies focused on maintaining a safe front clearance of a vehicle while only few considered the vehicle’s rear clearance. However, an intelligent vehicle may be collided by a following vehicle due to wrong manoeuvres of an unskilled driver of the following vehicle. Hence, it is essential for an intelligent vehicle to maintain a safe rear clearance when there is potential for a rear-end collision caused by a following vehicle. In this study, a rear-end collision escape algorithm is proposed to prevent rear-end collisions by a following vehicle considering both straight and curved roads. A trajectory planning method is designed according to the motions of the considered intelligent vehicle and the corresponding adjacent vehicles. The successive linearization and the Model Predictive Control (MPC) algorithms are used to design a motion controller in the proposed algorithm. Simulations were performed to demonstrate the effectiveness of the proposed algorithm. The results show that the proposed algorithm is effective in preventing rear-end collisions caused by a following vehicle. First published online 18 January 202

The relationship between self-efficacy and aggressive behavior in boxers: the mediating role of self-control

P. 1-9El comportamiento agresivo ha sido uno de los temas centrales de la psicología deportiva, mientras que el comportamiento agresivo de los boxeadores ha recibido una atención limitada. Aunque algunas publicaciones informaron que la autoeficacia se relaciona con el comportamiento agresivo, el mecanismo por el cual la autoeficacia afecta el comportamiento agresivo no está claro. El presente estudio investigó la relación entre la autoeficacia y el comportamiento agresivo, así como el efecto del autocontrol como factor mediador. Este estudio utiliza la Escala de autoeficacia para atletas, el Cuestionario de autocontrol para atletas y el Cuestionario de agresión de Buss-Perry. Esta relación se explora a través de medidas auto-informadas de N = 414 boxeadores profesionales chinos, n = 243 eran hombres y n = 171 mujeres, la edad promedio fue M = 17.72 años (SD = 3.147), los participantes, el número promedio de los años de ejercicio fueron M = 3.89 años (SD = 2.734); Los resultados mostraron que los boxeadores masculinos reportaron mayor agresividad que las boxeadoras; se encontró que la autoeficacia y el autocontrol mejoraron a medida que aumentaba la edad de los participantes; A mayor nivel de competencia, mayores niveles de autoeficacia y autocontrol; La autoeficacia se relacionó negativamente con el comportamiento agresivo y se correlacionó positivamente con el autocontrol. El autocontrol también se correlacionó negativamente con el comportamiento agresivo entre los boxeadores. El autocontrol tuvo un efecto de mediación total en la relación entre la autoeficacia y el comportamiento agresivoS

Traffic-Aware Ecological Cruising Control for Connected Electric Vehicle

The advent of intelligent connected technology has greatly enriched the capabilities of vehicles in acquiring information. The integration of short-term information from limited sensing range and long-term information from cloud-based systems in vehicle motion planning and control has become a vital means to deeply explore the energy-saving potential of vehicles. In this study, a traffic-aware ecological cruising control (T-ECC) strategy based on a hierarchical framework for connected electric vehicles in uncertain traffic environments is proposed, leveraging the two distinct temporal-dimension information. In the upper layer that is dedicated for speed planning, a sustainable energy consumption strategy (SECS) is introduced for the first time. It finds the optimal economic speed by converting variations in kinetic energy into equivalent battery energy consumption based on long-term road information. In the lower layer, a synthetic rolling-horizon optimization control (SROC) is developed to handle real-time traffic uncertainties. This control approach jointly optimizes energy efficiency, battery life, driving safety, and comfort for vehicles under dynamically changing traffic conditions. Notably, a stochastic preceding vehicle model is presented to effectively capture the uncertainties in traffic during the driving process. Finally, the proposed T-ECC is validated through simulations in both virtual and real-world driving conditions. Results demonstrate that the proposed strategy significantly improves the energy efficiency of the vehicle

An Adaptive Motion Planning Technique for On-Road Autonomous Driving

This paper presents a hierarchical motion planning approach based on discrete optimization method. Well-coupled longitudinal and lateral planning strategies with adaptability features are applied for better performance of on-road autonomous driving with avoidance of both static and moving obstacles. In the path planning level, the proposed method starts with a speed profile designing for the determination of longitudinal horizon, then a set of candidate paths will be constructed with lateral offsets shifting from the base reference. Cost functions considering driving comfort and energy consumption are applied to evaluate each candidate path and the optimal one will be selected as tracking reference afterwards. Re-determination of longitudinal horizon in terms of relative distance between ego vehicle and surrounding obstacles, together with update of speed profile, will be triggered for re-planning if candidate paths ahead fail the safety checking. In the path tracking level, a pure-pursuit-based tracking controller is implemented to obtain the corresponding control sequence and further smooth the trajectory of autonomous vehicle. Simulation results demonstrate the effectiveness of the proposed method and indicate its better performance in extreme traffic scenarios compared to traditional discrete optimization methods, while balancing computational burden at the same time