Divergent Roles of the CRH Receptors in the Control of Gonadotropin Secretion Induced by Acute Restraint Stress at Proestrus

CRH has been implicated as a mediator of stress-induced effects on the hypothalamus-pituitary-gonad axis, acting via CRH receptors in various brain regions. We investigated whether the effects of restraint stress on the secretion of gonadotropins on the morning of proestrus are mediated by the CRH-R1 or CRH-R2 receptors in the oval subdivision of the anterolateral BST, the central amygdala, the locus coeruleus (LC), or the A1 and A2 neuron groups in the medulla. At proestrus morning, rats were injected with antalarmin (a CRH-R1 antagonist), asstressin2-B (a CRH-R2 antagonist) or vehicles. Thirty minutes after the injection, the animals were placed into restraints for 30 min, and blood was sampled for 2 h. At the end of the experiment, the brains were removed for immunofluorescence analyses. Restraint stress increased the levels of FSH and LH. Antalarmin blocked the stress-induced increases in FSH and LH secretion, but astressin2-B only blocked the increase in FSH secretion. LC showed intense stress-induced neuronal activity. FOS/tyrosine-hydroxylase coexpression in LC was reduced by antalarmin, but not astressin2-B. The CRH-R1 receptor, more than CRH-R2 receptor, appears to be essential for the stimulation of the hypothalamus-pituitary-gonad axis by acute stress; this response is likely mediated in part by noradrenergic neurons in the LC. We postulate that the stress-induced facilitation of reproductive function is mediated, at least in part, by CRH action through CRH-R1 on noradrenaline neurons residing in the LC that trigger GnRH discharge and gonadotropin secretion. (Endocrinology 153: 4838-4848, 2012)

A New Control Architecture for Robust Controllers in Rear Electric Traction Passenger HEVs

It is well known that control systems are the core of electronic differential systems (EDSs) in electric vehicles (EVs)/hybrid HEVs (HEVs). However, conventional closed-loop control architectures do not completely match the needed ability to reject noises/disturbances, especially regarding the input acceleration signal incoming from the driver's commands, which makes the EDS (in this case) ineffective. Due to this, in this paper, a novel EDS control architecture is proposed to offer a new approach for the traction system that can be used with a great variety of controllers (e. g., classic, artificial intelligence (AI)-based, and modern/robust theory). In addition to this, a modified proportional-integral derivative (PID) controller, an AI-based neuro-fuzzy controller, and a robust optimal H-infinity controller were designed and evaluated to observe and evaluate the versatility of the novel architecture. Kinematic and dynamic models of the vehicle are briefly introduced. Then, simulated and experimental results were presented and discussed. A Hybrid Electric Vehicle in Low Scale (HELVIS)-Sim simulation environment was employed to the preliminary analysis of the proposed EDS architecture. Later, the EDS itself was embedded in a dSpace 1103 high-performance interface board so that real-time control of the rear wheels of the HELVIS platform was successfully achieved.

Numerical analysis of control surface effects on AUV

Computational fluid dynamics, CFD, is becoming an essential tool in the prediction of the hydrodynamic efforts and flow characteristics of underwater vehicles for manoeuvring studies. However, when applied to the manoeuvrability of autonomous underwater vehicles, AUVs, most studies have focused on the de- termination of static coefficients without considering the effects of the vehicle control surface deflection. This paper analyses the hydrodynamic efforts generated on an AUV considering the combined effects of the control surface deflection and the angle of attack using CFD software based on the Reynolds-averaged Navier–Stokes formulations. The CFD simulations are also independently conducted for the AUV bare hull and control surface to better identify their individual and interference efforts and to validate the simulations by comparing the experimental results obtained in a towing tank. Several simulations of the bare hull case were conducted to select the k –ω SST turbulent model with the viscosity approach that best predicts its hydrodynamic efforts. Mesh sensitivity analyses were conducted for all simulations. For the flow around the control surfaces, the CFD results were analysed according to two different methodologies, standard and nonlinear. The nonlinear regression methodology provides better results than the standard methodology does for predicting the stall at the control surface. The flow simulations have shown that the occurrence of the control surface stall depends on a linear relationship between the angle of attack and the control surface deflection. This type of information can be used in designing the vehicle’s autopilot system.