With the prosperity of artificial intelligence, more and more jobs will be replaced by robots. The future of precision agriculture (PA) will rely on autonomous robots to perform various agricultural operations. Real time kinematic (RTK) assisted global positioning system (GPS) is able to provide very accurate localization information with a detection error less than ±2 cm under ideal conditions. Autonomously driving a robotic vehicle within a furrow requires relative localization of the vehicle with respect to the furrow centerline. This relative location acquisition requires both the coordinates of the vehicle as well as all the stalks of the crop rows on both sides of the furrow. This extensive number of coordinate acquisitions of all the crop stalks demand onerous geographical survey of entire fields in advance. Additionally, real-time RTK-GPS localization of moving vehicles may suffer from satellite occlusion. Hence, the abovementioned ±2 cm accuracy is often significantly compromised in practice. Against this background, we propose sets of computer vision algorithms to coordinate with a low-cost camera (50 US dollars) and a LiDAR sensor (1500 US dollars) to detect the relative location of the vehicle in the furrow during early and late growth season respectively. Our solution package is superior than most current computer vision algorithms used for PA, thanks to its improved features, such as a machine-learning enabled dynamic crop recognition threshold, which adaptively adjusts its value according to the environmental changes like ambient light and crop size. Our in-field tests prove that our proposed algorithms approach the accuracy of an ideal RTK-GPS on cross-track detection, and exceed the ideal RTK-GPS on heading detection. Moreover, our solution package neither relies on satellite communication nor advance geographical surveys. Therefore, our low-complexity and low-cost solution package is a promising localization strategy as it is able to provide the same level of accuracy as an ideal RTK-GPS, yet more consistently and more reliably, as it requires no external conditions or hassle of the work demanded by RTK-GPS.

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We present a system for integrating multiple sources of data for finding missing persons. It can help authorities find children, developmentally challenged individuals who have wandered off, and persons of interest in investigations.

BACKGROUNDSoy protein isolate (SPI) is widely used in the food industry because of its nutritional and functional properties. During food processing and storage, the interaction with co‐existing sugars can cause changes in the structural and functional properties of SPI. In this study, SPI–l‐arabinose conjugate (SPI:Ara) and SPI–d‐galactose conjugate (SPI:Gal) were prepared using Maillard reaction (MR), and the effects of five‐carbon/six‐carbon sugars on the structural information and function of SPI were compared.RESULTSMR unfolded and stretched the SPI, changing its ordered conformation into disorder. Lysine and arginine of SPI were bonded with the carbonyl group of sugar. The MR between SPI and l‐arabinose has a higher degree of glycosylation compared to d‐galactose. MR of SPI enhanced its solubility, emulsifying property and foaming property. Compared with SPI:Ara, SPI:Gal exhibited better aforementioned properties. The functionalities of amphiphilic SPI were enhanced by MR, SPI:Gal possessed better hypoglycemic effect, fat binding capacity and bile acid binding ability than SPI:Ara. MR endowed SPI with enhanced biological activities, SPI:Ara showed higher antioxidant activities, and SPI:Gal exhibited stronger antibacterial activities.CONCLUSIONOur work revealed that l‐arabinose/d‐galactose exhibited different effects on the structural information of SPI, and further affected its physicochemical and functional property. © 2023 Society of Chemical Industry.

As the underwater structures of offshore Jacket platforms are always immersed in seawater, the marine growth, which include various forms of algae, slime, and seaweed, barnacles, mussels and other species of adhesive shellfish, will attach to steel-pipe surface and will accelerate structural corrosion and impair structural safety. Currently, routine cleaning and inspection task is undertaken by divers using cleaning jets in normally every 3∼5 years. The cleaning duration for one single platform will take up more than two months, even up to half a year, due to the constraints of weather windows and limited working hours of divers. It is a risky job for divers not only because of huge pressures that water-jets produce, but also the harsh working conditions of poor visibility, unexpected vortex and waves around platform, and etc. Underwater robots are being developed for various applications in offshore oil industry ranging from inspection to maintenance and cleaning of submerged surfaces and constructions. This paper introduces a novel underwater robot specializing in cleaning marine growth for offshore Jackets. Since the diameter of steel-pipes varies from about 600mm to 2000mm, a self-adapted mechanism is designed. The self-adaption mechanism makes the robot travel on pipes in different directions with high mobility and clean continuous region of underwater pipes’ surface at the same time. Two key issues have been studied in this paper. The magnetic adhesion method is adopted in the robot. A sensitivity study on the distance between steel-pipes and thickness of steel pipes with the adhesion force are conducted both using finite element method and experiments. Besides, the flushing capability for various nozzles has been simulated using computational fluid dynamics method. The proposed underwater robot is needed in the inspection and maintenance of offshore Jacket platforms. Compared with traditional maintenance by divers, it is more efficient, economic and safe.