Plant Copper Amine Oxidases: Key Players in Hormone Signaling Leading to Stress-Induced Phenotypic Plasticity

Polyamines are ubiquitous, low-molecular-weight aliphatic compounds, present in living organisms and essential for cell growth and differentiation. Copper amine oxidases (CuAOs) oxidize polyamines to aminoaldehydes releasing ammonium and hydrogen peroxide, which participates in the complex network of reactive oxygen species acting as signaling molecules involved in responses to biotic and abiotic stresses. CuAOs have been identified and characterized in different plant species, but the most extensive study on a CuAO gene family has been carried out in Arabidopsis thaliana. Growing attention has been devoted in the last years to the investigation of the CuAO expression pattern during development and in response to an array of stress and stress-related hormones, events in which recent studies have highlighted CuAOs to play a key role by modulation of a multilevel phenotypic plasticity expression. In this review, the attention will be focused on the involvement of different AtCuAOs in the IAA/JA/ABA signal transduction pathways which mediate stress-induced phenotypic plasticity events.     

Functional Coupling of Slack Channels and P2X3 Receptors Contributes to Neuropathic Pain Processing

The sodium-activated potassium channel Slack (K_Na1.1, Slo2.2, or Kcnt1) is highly expressed in populations of sensory neurons, where it mediates the sodium-activated potassium current (I_KNa) and modulates neuronal activity. Previous studies suggest that Slack is involved in the processing of neuropathic pain. However, mechanisms underlying the regulation of Slack activity in this context are poorly understood. Using whole-cell patch-clamp recordings we found that Slack-mediated I_KNa in sensory neurons of mice is reduced after peripheral nerve injury, thereby contributing to neuropathic pain hypersensitivity. Interestingly, Slack is closely associated with ATP-sensitive P2X3 receptors in a population of sensory neurons. In vitro experiments revealed that Slack-mediated I_KNa may be bidirectionally modulated in response to P2X3 activation. Moreover, mice lacking Slack show altered nocifensive responses to P2X3 stimulation. Our study identifies P2X3/Slack signaling as a mechanism contributing to hypersensitivity after peripheral nerve injury and proposes a potential novel strategy for treatment of neuropathic pain.     

Deterministic and probabilistic ship pitch prediction using a multi-predictor integration model based on hybrid data preprocessing,reinforcement learning and improved QRNN

The deterministic and probabilistic prediction of ship motion is important for safe navigation and stable real-time operational control of ships at sea. However, the volatility and randomness of ship motion, the non-adaptive nature of single predictors and the poor coverage of quantile regression pose serious challenges to uncertainty prediction, making research in this field limited. In this paper, a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and improved quantile regression neural network (QRNN) is proposed to explore the deterministic and probabilistic prediction of ship pitch motion. To validate the performance of the proposed multi-predictor integrated prediction model, an experimental study is conducted with three sets of actual ship longitudinal motions during sea trials in the South China Sea. The experimental results indicate that the root mean square errors (RMSEs) of the proposed model of deterministic prediction are 0.0254°, 0.0359°, and 0.0188°, respectively. Taking series #2 as an example, the prediction interval coverage probabilities (PICPs) of the proposed model of probability predictions at 90%, 95%, and 99% confidence levels (CLs) are 0.9400, 0.9800, and 1.0000, respectively. This study signifies that the proposed model can provide trusted deterministic predictions and can effectively quantify the uncertainty of ship pitch motion, which has the potential to provide practical support for ship early warning systems.     

低比转速压气机轮毂轮缘型线多目标优化设计

对电动增压器用低比转速离心压气机轮毂轮缘进行优化设计。采用NUMECA/FINE Design 3D对压气机进行几何参数化,生成数据库样本,利用人工神经网络和遗传算法对轮毂轮缘型线进行优化,并进行流场分析。仿真结果表明,在设计转速为40000 r/min时,优化后设计点压比和效率分别提高6.59%和3.73%。     

滚齿工艺对齿根粗糙度的影响

针对国内外风电和工业齿轮箱轻量化的设计趋势,较低的安全系数对齿轮齿根弯曲强度提出了更为严格的要求。研究表明,在材料技术和齿形设计难以再优化的情况下,滚齿加工的粗糙度,尤其是齿根部位的粗糙度对齿轮弯曲强度的影响尤为敏感。针对滚齿工艺过程,从滚齿加工参数、刀具前刀面粗糙度和刀具刃口圆角三方面进行分析,验证其对齿根粗糙度的影响。     

The vibration and pull-in mechanism of two coupled elastically restrained beams assembly subjected to electrostatic force

ABSTRACT

The novel model of the coupled beam-type electrodes with elastic roots, subjected to the AC and DC voltages is constructed. The exact critical pull-in voltage of the two coupled elastically restrained beams with size effect is presented. The analytical method of the general system subjected to the AC and DC voltages simultaneously is presented. The characteristic mechanism of coupled vibration is investigated.     

基于机器视觉的机车齿轮毂全自动双轴拧紧机的设计和应用

设计了基于机器视觉的机车齿轮毂全自动双轴拧紧机,根据拧紧工艺和扭矩控制原理实现对螺栓拧紧力矩的控制;通过丝杠变径机构,实现不同规格齿轮毂螺栓拧紧;利用机器视觉实现螺栓位置的识别以及初始位置的确定;讨论了自动拧紧机控制系统的硬件组成和软件系统配置,并介绍了防错、防漏功能的设计流程。螺栓自动拧紧机可提高装配精度和操作效率,降低工人的劳动强度。     

Risk-constrained probabilistic optimal scheduling of FCPP-CHP based energy hub considering demand-side resources

Renewable energy sources (RES) with sharing a large percentage of future energy generation capacities play an essential role in the decarbonization of the future electricity and thermal networks as well as transportation sectors. However, the uncertainties in their outputs make some difficulties in making operational decisions. Hydrogen energy plays a considerable role in this concept. Besides, energy hubs (EHs) provide an efficient and reliable framework for gathering multi-type energy carriers.This paper optimally schedules the operating of the EH and decreases the emission cost, considering the electrical and thermal demand response (DR) program in a probabilistic environment. Besides plug-in electric vehicles (PEVs) and a complete model of hydrogen-based renewable energy sources are presented in the EH. Taking into account uncertainties of electrical/thermal energy markets real-time prices, customers' energy demand, and energy production of RESs into account, various scenarios are generated using the Monte Carlo simulation technique. Next, an efficient method is used to reduce the number of the scenario to make the optimization problem computable and fast. In order to reduce the risk of encountering high operating costs, the conditional value at risk (CVaR) technique is used to manage the associated risk. Simulation results show the efficiency of the proposed method in decreasing the operational cost and managing the risk of encountering unfavorable states.