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In urban areas, dense buildings frequently block and reflect global positioning system (GPS) signals, resulting in the reception of a few visible satellites with many multipath signals. This is a significant problem that results in unreliable positioning in urban areas. If a signal reception condition from a certain satellite can be detected, the positioning performance can be improved by excluding or de-weighting the multipath contaminated satellite signal. Thus, we developed a machine-learning-based method of classifying GPS signal reception conditions using a dual-polarized antenna. We employed a decision tree algorithm for classification using three features, one of which can be obtained only from a dual-polarized antenna. A machine-learning model was trained using GPS signals collected from various locations. When the features extracted from the GPS raw signal are input, the generated machine-learning model outputs one of the three signal reception conditions: non-line-of-sight (NLOS) only, line-of-sight (LOS) only, or LOS+NLOS. Multiple testing datasets were used to analyze the classification accuracy, which was then compared with an existing method using dual single-polarized antennas. Consequently, when the testing dataset was collected at different locations from the training dataset, a classification accuracy of 64.47% was obtained, which was slightly higher than the accuracy of the existing method using dual single-polarized antennas. Therefore, the dual-polarized antenna solution is more beneficial than the dual single-polarized antenna solution because it has a more compact form factor and its performance is similar to that of the other solution., Comment: Submitted to IEEE ION PLANS 2023

Opportunities for the exposure of pregnant women to engineered nanoparticles have been increasing with the expanding use of these materials. Therefore, there are concerns that nanoparticles could have adverse effects on the establishment and maintenance of pregnancy. The effects of nanoparticles on the mother and fetus have been evaluated from this perspective, but there is still little knowledge about the effects on placentation and function acquisition, which are essential for the successful establishment and maintenance of pregnancy. Formation of the syncytiotrophoblast is indispensable for the acquisition of placental function, and impairment of syncytialization inevitably affects pregnancy outcomes. Here, we assessed the effect of nanoparticles on placental formation by using forskolin-treated BeWo cells, a typical in vitro model of trophoblast syncytialization. Immunofluorescence staining analysis revealed that silver nanoparticles with a diameter of 10 nm (nAg10) (at 0.156 µg/mL) significantly decreased the proportion of syncytialized BeWo cells, but gold nanoparticles with a diameter of 10 nm did not. Consistently, only nAg10 (at 0.156 µg/mL) significantly suppressed forskolin-induced elevation of CGB and SDC1 mRNA expression levels and human chorionic gonadotropin β production in a dose-dependent manner; these molecules are all markers of syncytialization. Besides, nAg10 significantly decreased the expression of ERVFRD-1, which encodes proteins associated with cell fusion. Moreover, nAg10 tended to suppress the expression of sFlt-1 e15a, a placental angiogenesis marker. Collectively, our data suggest that nAg10 could suppress formation of the syncytiotrophoblast and that induce placental dysfunction and the following poor pregnancy outcomes.

An integrity-based path planning strategy for autonomous ground vehicle (AGV) navigation in urban environments is developed. The vehicle is assumed to navigate by utilizing cellular long-term evolution (LTE) signals in addition to Global Positioning System (GPS) signals. Given a desired destination, an optimal path is calculated, which minimizes a cost function that considers both the horizontal protection level (HPL) and travel distance. The constraints are that (i) the ratio of nodes with faulty signals to the total nodes be lower than a maximum allowable ratio and (ii) the HPLs along each candidate path be lower than the horizontal alert limit (HAL). To predict the faults and HPL before the vehicle is driven, GPS and LTE pseudoranges along the candidate paths are generated utilizing a commercial ray-tracing software and three-dimensional (3D) terrain and building maps. Simulated pseudoranges inform the path planning algorithm about potential biases due to reflections from buildings in urban environments. Simulation results are presented showing that the optimal path produced by the proposed path planning strategy has the minimum average HPL among the candidate paths.