Search

In the era of industrial evolution, satellites are being viewed as swarm intelligence that does not rely on a single system but multiple constellations that collaborate autonomously. This has enhanced the potential of the Global Navigation Satellite System (GNSS) to contribute to improving position, navigation, and timing (PNT) services. However, multipath (MP) and non-line-of-sight (NLOS) receptions remain the prominent vulnerability for the GNSS in harsh environments. The aim of this research is to investigate the impact of MP and NLOS receptions on GNSS performance and then propose a Received Signal Quality Monitoring (RSQM) algorithm. The RSQM algorithm works in two ways. Initially, it performs a signal quality test based on a fuzzy inference system. The input parameters are carrier-to-noise ratio (CNR), Normalized Range Residuals (NRR), and Code–Carrier Divergence (CCD), and it computes the membership functions based on the Mamdani method and classifies the signal quality as LOS, NLOS, weak NLOS, and strong NLOS. Secondly, it performs an adaptive navigation strategy to exclude/mask the affected range measurements while considering the satellite geometry constraints (i.e., D O P ≤ 2 ). For this purpose, comprehensive research to quantify the multi-constellation GNSS receiver with four constellation configurations (GPS, BeiDou, GLONASS, and Galileo) has been carried out in various operating environments. This RSQM-based GNSS receiver has the capability to identify signal quality and perform adaptive navigation accordingly to improve navigation performance. The results suggest that GNSS performance in terms of position error is improved from 5.4 m to 2.3 m on average in the complex urban environment. Combining the RSQM algorithm with the GNSS has great potential for the future industrial revolution (Industry 5.0), making things automatic and sustainable like autonomous vehicle operation. [ABSTRACT FROM AUTHOR]

Quantum orthogonal arrays are combinatorial designs with a remarkable class of genuinely multipartite highly entangled states. In this paper, we propose the method for constructing maximally multi-qubit entangled state through phase parameters of orthogonal arrays. Using this method, we not only determine that maximally n-qubit entangled state, but also find the n − 1 orthogonal array that gives maximally n − 1 entangled states. Such states play an important role in quantum theory with a high degree of multipartite entanglement. [ABSTRACT FROM AUTHOR]

Background: The use of lithium during breast‐feeding has not been comprehensively investigated in humans due to concerns about lithium toxicity. Procedure: We analyzed lithium in the kidneys of nursed pups of lithium medicated mothers, using analytical spectroscopy in a novel rat model. The mothers were healthy rats administered lithium via gavage (1000 mg/day Li2CO3 per 50 kg body weight). Results: Lithium was detected in the breast milk, and in the blood of pups (0.08 mM), of lithium‐exposed dams at post‐natal day 18 (P18), during breast‐feeding. No lithium was detected after breast‐feeding, at P25 (4 days after cessation of nursing). The lithium pups blood had elevated urea nitrogen at P18 and reduced total T4 at P18 and P25, indicating a longer‐term effect on the kidneys and the thyroid gland. Multivariate machine‐learning analysis of spectroscopy data collected from the excised kidneys of pups showed elevated potassium in lithium‐exposed animals both during‐ and after breast‐feeding. The elevated renal potassium was associated with low nephrin expression in the kidneys measured immunohistochemically during breast‐feeding. After lithium exposure is stopped, the filtration of lithium from the kidneys reverses these effects. Our study showed that breastfeeding during lithium use has an effect on the kidneys of the offspring in rats. [ABSTRACT FROM AUTHOR]

Advancement in depth-sensing technology has allowed mobile robots to visualize the surrounding environment in 3D models. Regardless of the sensing technology (i.e. active, passive, or laser-based), a complete system that integrates recent depth data in previous 3D models in real-time is done by employing Simultaneous Localization And Mapping (SLAM) algorithms followed by a 3D reconstruction engine. Unfortunately, both the SLAM algorithm and the 3D reconstruction engine are usually executed on a single computing device, making the whole system exceptionally costly and heavy and restricting the robot's mobility. This paper proposes a decentralized, modular reconstruction system capable of employing various sensors to facilitate online 3D reconstruction from a resource-limited mobile robot. [ABSTRACT FROM AUTHOR]