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Loperamide reverses signs of mechanical hypersensitivity in an animal model of neuropathic pain suggesting that peripheral opioid receptors may be suitable targets for the treatment of neuropathic pain. Since little is known about loperamide effects on the responsiveness of primary afferent nerve fibers, in vivo electrophysiological recordings from unmyelinated afferents innervating the glabrous skin of the hind paw were performed in rats with an L5 spinal nerve lesion or sham surgery. Mechanical threshold and responsiveness to suprathreshold stimulation were tested before and after loperamide (1.25, 2.5 and 5 µg in 10 µl) or vehicle injection into the cutaneous receptive field. Loperamide dose-dependently decreased mechanosensitivity in unmyelinated afferents of nerve-injured and sham animals, and this effect was not blocked by naloxone pretreatment. We then investigated loperamide effects on nerve conduction by recording compound action potentials in vitro during incubation of the sciatic nerve with increasing loperamide concentrations. Loperamide dose-dependently decreased compound action potentials of myelinated and unmyelinated fibers (ED50 = 8 and 4 µg/10 µl, respectively). This blockade was not prevented by pre-incubation with naloxone. These results suggest that loperamide reversal of behavioral signs of neuropathic pain may be mediated, at least in part, by mechanisms independent of opioid receptors, most probably by local anesthetic actions.

Different classes of unmyelinated nerve fibers appear to exhibit distinct conductive properties. We sought a criterion based on conduction properties for distinguishing sympathetic efferents and unmyelinated, primary afferents in peripheral nerves.In anesthetized monkey, centrifugal or centripetal recordings were made from single unmyelinated nerve fibers in the peroneal or sural nerve, and electrical stimuli were applied to either the sciatic nerve or the cutaneous nerve endings, respectively. In centrifugal recordings, electrical stimulation at the sympathetic chain and dorsal root was used to determine the fiber's origin. In centrifugal recordings, sympathetic fibers exhibited absolute speeding of conduction to a single pair of electrical stimuli separated by 50 ms; the second action potential was conducted faster (0.61 0.16%) than the first unconditioned action potential. This was never observed in primary afferents. Following 2 Hz stimulation (3 min), activity-dependent slowing of conduction in the sympathetics (8.6 0.5%) was greater than in one afferent group (6.7 0.5%) but substantially less than in a second afferent group (29.4 1.9%). In centripetal recordings, most mechanically-insensitive fibers also exhibited absolute speeding to twin pulse stimulation. The subset that did not show this absolute speeding was responsive to chemical stimuli (histamine, capsaicin) and likely consists of mechanically-insensitive afferents. During repetitive twin pulse stimulation, mechanosensitive afferents developed speeding, and speeding in sympathetic fibers increased.The presence of absolute speeding provides a criterion by which sympathetic efferents can be differentiated from primary afferents. The differences in conduction properties between sympathetics and afferents likely reflect differential expression of voltage-sensitive ion channels.

Current state-of-the-art (SOTA) LiDAR-only detectors perform well for 3D object detection tasks, but point cloud data are typically sparse and lacks semantic information. Detailed semantic information obtained from camera images can be added with existing LiDAR-based detectors to create a robust 3D detection pipeline. With two different data types, a major challenge in developing multi-modal sensor fusion networks is to achieve effective data fusion while managing computational resources. With separate 2D and 3D feature extraction backbones, feature fusion can become more challenging as these modes generate different gradients, leading to gradient conflicts and suboptimal convergence during network optimization. To this end, we propose a 3D object detection method, Attention-Enabled Point Fusion (AEPF). AEPF uses images and voxelized point cloud data as inputs and estimates the 3D bounding boxes of object locations as outputs. An attention mechanism is introduced to an existing feature fusion strategy to improve 3D detection accuracy and two variants are proposed. These two variants, AEPF-Small and AEPF-Large, address different needs. AEPF-Small, with a lightweight attention module and fewer parameters, offers fast inference. AEPF-Large, with a more complex attention module and increased parameters, provides higher accuracy than baseline models. Experimental results on the KITTI validation set show that AEPF-Small maintains SOTA 3D detection accuracy while inferencing at higher speeds. AEPF-Large achieves mean average precision scores of 91.13, 79.06, and 76.15 for the car class’s easy, medium, and hard targets, respectively, in the KITTI validation set. Results from ablation experiments are also presented to support the choice of model architecture.

Safe autonomous vehicle (AV) operations depend on an accurate perception of the driving environment, which necessitates the use of a variety of sensors. Computational algorithms must then process all of this sensor data, which typically results in a high on-vehicle computational load. For example, existing lane markings are designed for human drivers, can fade over time, and can be contradictory in construction zones, which require specialized sensing and computational processing in an AV. But, this standard process can be avoided if the lane information is simply transmitted directly to the AV. High definition maps and road side units (RSUs) can be used for direct data transmission to the AV, but can be prohibitively expensive to establish and maintain. Additionally, to ensure robust and safe AV operations, more redundancy is beneficial. A cost-effective and passive solution is essential to address this need effectively. In this research, we propose a new infrastructure information source (IIS), chip-enabled raised pavement markers (CERPMs), which provide environmental data to the AV while also decreasing the AV compute load and the associated increase in vehicle energy use. CERPMs are installed in place of traditional ubiquitous raised pavement markers along road lane lines to transmit geospatial information along with the speed limit using long range wide area network (LoRaWAN) protocol directly to nearby vehicles. This information is then compared to the Mobileye commercial off-the-shelf traditional system that uses computer vision processing of lane markings. Our perception subsystem processes the raw data from both CEPRMs and Mobileye to generate a viable path required for a lane centering (LC) application. To evaluate the detection performance of both systems, we consider three test routes with varying conditions. Our results show that the Mobileye system failed to detect lane markings when the road curvature exceeded ±0.016 m−1. For the steep curvature test scenario, it could only detect lane markings on both sides of the road for just 6.7% of the given test route. On the other hand, the CERPMs transmit the programmed geospatial information to the perception subsystem on the vehicle to generate a reference trajectory required for vehicle control. The CERPMs successfully generated the reference trajectory for vehicle control in all test scenarios. Moreover, the CERPMs can be detected up to 340 m from the vehicle’s position. Our overall conclusion is that CERPM technology is viable and that it has the potential to address the operational robustness and energy efficiency concerns plaguing the current generation of AVs.

High growth rates and rapid reproductive development and associated decline in feed quality of sown tropical perennial grass pastures present management challenges for livestock producers. Conservation of surplus forage as silage could be an effective management tool. Experiments were conducted to evaluate the fermentation quality of silages produced from tropical grasses. Five species (Chloris gayana, Megathyrsus maximus, Panicum coloratum, Digitaria eriantha and Cenchrus clandestinus) were ensiled without additives after a short, effective wilt at dry matter (DM) contents ranging from 302.4 to 650.1 g kg−1. The fermentation profile of all silages in 2019 was typical for high DM silages, but in 2020 ammonia (% of total nitrogen: NH3-N), acetic acid and pH levels were higher. In 2020 M. maximus (302.4 g kg−1 DM) was poorly preserved with 20.2% NH3-N. The DM content of all other silages exceeded 350 g kg−1 and fermentation quality was generally good. In a second experiment, M. maximus was ensiled at 365 g kg−1 chopped and 447 g kg−1 DM chopped and unchopped, either without or with Pioneer 1171® (Lactobacillus plantarum and Enterococcus faecium) or Lallemand Magniva Classic® (L. plantarum and Pediococcus pentasaceus) bacterial inoculant. Inoculants increased lactic acid production, reduced pH and improved fermentation compared to Control, but D-lactate, L-lactate and acetic acid production differed between inoculants. Unchopped silages had higher pH and NH3-N and better preserved protein fraction than chopped silages at the same DM content. In both experiments, wilting increased water soluble carbohydrates by 0.5–31.5 g kg−1 DM and ensiling increased degradation of the protein fraction. We concluded that a rapid and effective wilt combined with a bacterial additive resulted in well preserved tropical grass silages.

This work investigates the fuel energy and emission reductions possible with the hybridization of a Class 8 tractor-trailer. The truck tractor has two drive axles: one powered by an internal-combustion-engine-based powertrain (CP) and the other powered by an electric powertrain (EP) consisting of an electric drive system supplied by a battery pack, resulting in a through-the-road hybrid. The EP has two modes of operation depending on the direction of power flow: motoring/battery discharging and generating/battery recharging. Switched optimal control is used to select between the two modes of EP operation, and a recently developed distributed switched optimal control is applied. The control is distributed between the CP, the EP, and the vehicle motion operation components. Control-oriented, component-specific power flow models are set forth to describe the dynamics and algebraic relationships. Four different tractor-trailers are simulated: the original CP and three hybrids with engine sizes of 15 L, 11 L, and 7 L. Simulations are performed over a short test cycle and two regulatory driving cycles to compare the fuel use, total energy, and emissions. Results show that the hybrids have reduced fuel use, total energy, and emissions compared to the original CP; the reductions and reference velocity tracking error increases as the engine size is decreased. Particularly, fuel use is reduced by at least 4.1% under a charge sustaining operation and by 9.8% when the battery energy can be restored with an off-board charger at the end of the cycle.

Distributed control is investigated to solve an electric vehicle switched optimal control problem faster than centralized control without significant performance change. The powertrain includes a cooling system, supercapacitor, and two switched mode components: a battery with discharging and charging modes and an electric drive with motoring and generating modes. Control-oriented component power flow models are developed with mode and temperature dependence. Component specific power and thermal management optimization problems, subject to these models, require solution for overall powertrain management. The power management problem is switched, having discrete-valued mode selection variables. Both problems are solved in a distributed manner using the alternating direction method of multipliers (ADMM). An ADMM-based algorithm to solve the switched powertrain management problem is proposed; it (i) solves the embedded version of the switched problem that relaxes discrete mode switch values to continuous values and (ii) projects embedded mode selection values onto discrete values and then solves the problem with now known mode selections. The distributed solution approach is demonstrated using a trapezoidal drive profile and three regulatory profiles. The regulatory results are compared to centralized control and the proposed algorithm achieved at least a 3.3 times improvement in solution time with comparable drive performance.