A novel Nb superconducting joint with ultralow resistance fabricated by electron beam welding method for superconducting gravimeter

This paper presents a novel Nb superconducting joint with an ultralow resistance of 7.9 × 10-16 Ω, fabricated using the electron beam welding (EBW) method. After the EBW process, the two Nb filaments formed a single joint with a much larger grain size and smaller grain misorientation. More importantly, the resistance of the EBW Nb joint was nearly one magnitude lower than that of most conventional pressing joint. The ultralow resistance is essential for superconducting gravimeters, which require an extremely low drift rate. The EBW Nb joint allowed the superconducting gravimeter to have a much better performance when applied in the field of structural geology, geodesy, microgravity, and metrology. We believe that the EBW method could be one of the most promising joint fabrication methods for achieving maximum stability (less than 1 μgal/yr).

LLIO: Lightweight Learned Inertial Odometer

<div><div>The 3D position estimation of pedestrians is a vital module to build the connections between persons and things.</div><div>The traditional gait model-based methods cannot fulfill the various motion patterns.</div><div>And the various data-driven-based inertial odometry solutions focus on the 2D trajectory estimation on the ground plane, which is not suitable for AR applications.</div><div>TLIO (Tight Learned Inertial Odometry) proposed an inertial-based 3D motion estimator that achieves very low position drift by using the raw IMU measurements and the displacement predict coming from a neural network to provide low drift pedestrian dead reckoning.</div><div>However, TLIO is unsuitable for mobile devices because it is computationally expensive.</div><div>In this paper, a lightweight learned inertial odometry network (LLIO-Net) is designed for mobile devices.</div><div>By replacing the network in TLIO with the LLIO-Net, the proposed system shows similar accuracy but significantly improved efficiency.</div><div>Specifically, the proposed LLIO algorithm was implemented on mobile devices and compared the efficiency with TLIO.</div><div>The inference efficiency of the proposed system is 2-12 times that of TLIO.</div></div>

LLIO: Lightweight Learned Inertial Odometer

<div><div>The 3D position estimation of pedestrians is a vital module to build the connections between persons and things.</div><div>The traditional gait model-based methods cannot fulfill the various motion patterns.</div><div>And the various data-driven-based inertial odometry solutions focus on the 2D trajectory estimation on the ground plane, which is not suitable for AR applications.</div><div>TLIO (Tight Learned Inertial Odometry) proposed an inertial-based 3D motion estimator that achieves very low position drift by using the raw IMU measurements and the displacement predict coming from a neural network to provide low drift pedestrian dead reckoning.</div><div>However, TLIO is unsuitable for mobile devices because it is computationally expensive.</div><div>In this paper, a lightweight learned inertial odometry network (LLIO-Net) is designed for mobile devices.</div><div>By replacing the network in TLIO with the LLIO-Net, the proposed system shows similar accuracy but significantly improved efficiency.</div><div>Specifically, the proposed LLIO algorithm was implemented on mobile devices and compared the efficiency with TLIO.</div><div>The inference efficiency of the proposed system is 2-12 times that of TLIO.</div></div>

Evaluation of Drift-Reducing Nozzles for Pesticide Application in Hazelnut (Corylus avellana L.)

Spraying pesticides using air induction nozzles is a well-known method to reduce drift. These drift-reducing nozzles have been tested on many different tree crops (such as apples, citrus, and grapes), but we are still lacking information on their utilization on hazelnut (Corylus avellana L.) groves, although hazelnut is a major nut crop in Italy, and in recent years its cultivated area has been constantly growing. This paper reports a comparison between treatments carried out with cone and flat-fan low-drift nozzles versus two conventional nozzles. The distribution quality, the number of droplets per cm2 of the target area, and the drift in non-target trees adjacent to those treated were evaluated by analyzing the impact of the droplets on water-sensitive papers placed on the tree canopies. The results show that because no significative differences in terms of application quality were found between the tested nozzles, low-drift nozzles can be a good alternative to the standard nozzles to reduce the drift of pesticide applications in hazelnuts without altering the chosen distribution of the pesticide.

Development and Prototype Testing of an Agricultural Nozzle Clog Detection Device

HighlightsPrototypes of an agricultural nozzle clog detection system (for 18 nozzles) have been successfully developed.Spray quality characteristics (droplet size, pattern, and coverage) were not significantly affected when testing the device with extended-range nozzles (TeeJet XR8004).Most of the spray quality characteristics were significantly affected when testing the device with ultra low-drift nozzles (John Deere PSULDQ2004).Abstract. Agricultural nozzles are the main components that perform the spraying of agrochemicals, and their proper functionality is a key element for uniform spray application on crops. Because nozzles have small orifices, they can become clogged when there is debris from the agrochemical in the tank. Nozzle clogging during spray application results in poor pest and weed management and increased cost for re-spraying the affected crop row. Measures used to prevent nozzles from clogging include using screens or strainers to filter out debris before it reaches the nozzle tip, as well as performing regular checks on the nozzles. However, nozzle clogging still occurs during spraying despite the precautions taken. Thus, a device that can detect nozzle clogging during spraying is necessary to enable a quicker response that will ensure uniform application across each row of the crop. A novel, patented device for detecting clogged nozzles that is externally attachable to each nozzle on a sprayer boom was developed in the Precision Application Technology Lab at Arkansas State University. The main objective of this article is to present a general description of this prototype nozzle clog detection device and the nozzle clog detection system. Spray droplet size and pattern tests under controlled conditions and spray coverage tests under field conditions were conducted with and without the device to determine if there were significant differences in droplet size, spray pattern, or spray coverage between using and not using the device. The tests demonstrated that this new technology has potential for detecting clogged nozzles without significantly influencing spray quality for extended-range nozzles but not for ultra low-drift nozzles. To increase the reliability of the performance of this new technology, further improvements in the design need to be considered. Keywords: Clogged nozzle, Detection, Droplet size, Prototype device, Spray coverage, Spray pattern.