Integrating Cosmic Microwave Background Readings with Celestial Navigation to Enhance Deep Space Navigation.

This research unveils a cutting-edge navigation system for deep space missions that utilizes cosmic microwave background (CMB) sensor readings to enhance spacecraft positioning and velocity estimation accuracy significantly. By exploiting the Doppler-shifted CMB spectrum and integrating it with optical measurements for celestial navigation, this approach employs advanced data processing through the Unscented Kalman Filter (UKF), enabling precise navigation amid the complexities of space travel. The simulation results confirm the system's exceptional precision and resilience in deep space missions, marking a significant advancement in astronautics and paving the way for future space exploration endeavors.

Chemoinformatic approaches for navigating large chemical spaces.

INTRODUCTION: Large chemical spaces (CSs) include traditional large compound collections, combinatorial libraries covering billions to trillions of molecules, DNA-encoded chemical libraries comprising complete combinatorial CSs in a single mixture, and virtual CSs explored by generative models. The diverse nature of these types of CSs require different chemoinformatic approaches for navigation. AREAS COVERED: An overview of different types of large CSs is provided. Molecular representations and similarity metrics suitable for large CS exploration are discussed. A summary of navigation of CSs in generative models is provided. Methods for characterizing and comparing CSs are discussed. EXPERT OPINION: The size of large CSs might restrict navigation to specialized algorithms and limit it to considering neighborhoods of structurally similar molecules. Efficient navigation of large CSs not only requires methods that scale with size but also requires smart approaches that focus on better but not necessarily larger molecule selections. Deep generative models aim to provide such approaches by implicitly learning features relevant for targeted biological properties. It is unclear whether these models can fulfill this ideal as validation is difficult as long as the covered CSs remain mainly virtual without experimental verification.

Cognitive Decline Associated with Aging.

Cognitive decline is one of the most distinct signs of aging, and age-related cognitive decline is a heterogeneous issue varying in different cognitive domains and has significant differences among older adults. Identifying characteristics of cognitive aging is the basis of cognitive disease for early-detection and healthy aging promotion. In the current chapter, age-related decline of main cognitive domains, including sensory perception, memory, attention, executive function, language, reasoning, and space navigation ability are introduced respectively. From these aspects of cognition, we focus on the age-related effects, age-related cognitive diseases, and possible mechanisms of cognitive aging.

Blue velvet arena test to detect spatial memory in patients with chronic insomnia disorder: a clinical study / 中华神经科杂志

Objective:To investigate the feasibility of blue velvet arena test (BVAT) in evaluating spatial memory function in patients with chronic insomnia disorder (CID).Methods:From June 1, 2021 to May 31, 2022, 62 CID outpatients or inpatients were enrolled continuously in the Department of Sleep Disorders, the Affiliated Chaohu Hospital of Anhui Medical University, and 56 good sleepers in the same period were enrolled to serve as controls. Pittsburgh Sleep Quality Index (PSQI) was used to assess their sleep quality. Montreal Cognitive Assessment Scale (MoCA), nine box maze test (NBMT), and BVAT were used to assess general cognition and memories.Results:Compared to the controls, the CID patients had increased PSQI score [15.0 (12.8, 16.0) vs 0 (0, 1.0); Z=-9.47, P<0.001], and decreased MoCA score [24.5 (21.5, 27.0) vs 27.0 (26.0, 28.0); Z=-4.18, P<0.001]; increased numbers of errors in the spatial working [1.0 (0.8, 2.0) vs 1.0 (0, 1.0); Z=-2.24, P<0.05], object working [1.5 (0.8, 3.0) vs 0 (0, 1.0); Z=-4.36, P<0.001] and object recognition [0 (0, 0) vs 0 (0, 0); Z=-2.10, P<0.05] memories in NBMT; and increased average erroring distance in BVAT [23.0 (16.4, 27.2) cm vs 18.7 (16.6, 20.7) cm; Z=-3.30, P<0.01]. Partial correlation analysis showed that in the CID patients, the average erroring distance in BVAT was positively correlated with erroneous numbers in spatial working memory in NBMT ( r=0.54, P<0.001). Principal components analysis showed that the average erroring distance of BVAT (load=0.844) and the errors of spatial working memory in NBMT (load=0.801) were jointly attributed to the first factor. Receiver operating characteristic curve analysis showed that the sensitivity of BVAT was higher than that of NBMT (0.575 vs 0.250, P<0.05) for spatial memory detection in total sample. Conclusion:The BVAT has a higher reliability in the functional assessment of spatial memory in CID patients.

Comparison of Deep Space Navigation Using Optical Imaging, Pulsar Time-of-Arrival Tracking, and/or Radiometric Tracking.

Recent advances with space navigation technologies developed by NASA in space-based atomic clocks and pulsar X-ray navigation, combined with past successes in autonomous navigation using optical imaging, brings to the forefront the need to compare space navigation using optical, radiometric, and pulsar-based measurements using a common set of assumptions and techniques. This review article examines these navigation data types in two different ways. First, a simplified deep space orbit determination problem is posed that captures key features of the dynamics and geometry, and then each data type is characterized for its ability to solve for the orbit. The data types are compared and contrasted using a semi-analytical approach with geometric dilution of precision techniques. The results provide useful parametric insights into the strengths of each data type. In the second part of the paper, a high-fidelity, Monte Carlo simulation of a Mars cruise, approach, and entry navigation problem is studied. The results found complement the semi-analytic results in the first part, and illustrate specific issues such as each data type's quantitative impact on solution accuracy and their ability to support autonomous delivery to a planet.

Bayesian models of human navigation behaviour in an augmented reality audiomaze.

We investigated Bayesian modelling of human whole-body motion capture data recorded during an exploratory real-space navigation task in an "Audiomaze" environment (see the companion paper by Miyakoshi et al. in the same volume) to study the effect of map learning on navigation behaviour. There were three models, a feedback-only model (no map learning), a map resetting model (single-trial limited map learning), and a map updating model (map learning accumulated across three trials). The estimated behavioural variables included step sizes and turning angles. Results showed that the estimated step sizes were constantly more accurate using the map learning models than the feedback-only model. The same effect was confirmed for turning angle estimates, but only for data from the third trial. We interpreted these results as Bayesian evidence of human map learning on navigation behaviour. Furthermore, separating the participants into groups of egocentric and allocentric navigators revealed an advantage for the map updating model in estimating step sizes, but only for the allocentric navigators. This interaction indicated that the allocentric navigators may take more advantage of map learning than do egocentric navigators. We discuss relationships of these results to simultaneous localization and mapping (SLAM) problem.

A 3D-Printed Self-Learning Three-Linked-Sphere Robot for Autonomous Confined-Space Navigation.

Reinforcement learning control methods can impart robots with the ability to discover effective behavior, reducing their modeling and sensing requirements, and enabling their ability to adapt to environmental changes. However, it remains challenging for a robot to achieve navigation in confined and dynamic environments, which are characteristic of a broad range of biomedical applications, such as endoscopy with ingestible electronics. Herein, a compact, 3D-printed three-linked-sphere robot synergistically integrated with a reinforcement learning algorithm that can perform adaptable, autonomous crawling in a confined channel is demonstrated. The scalable robot consists of three equally sized spheres that are linearly coupled, in which the extension and contraction in specific sequences dictate its navigation. The ability to achieve bidirectional locomotion across frictional surfaces in open and confined spaces without prior knowledge of the environment is also demonstrated. The synergistic integration of a highly scalable robotic apparatus and the model-free reinforcement learning control strategy can enable autonomous navigation in a broad range of dynamic and confined environments. This capability can enable sensing, imaging, and surgical processes in previously inaccessible confined environments in the human body.

Navigating Among Known Structures in Protein Space.

Present-day protein space is the result of 3.7 billion years of evolution, constrained by the underlying physicochemical qualities of the proteins. It is difficult to differentiate between evolutionary traces and effects of physicochemical constraints. Nonetheless, as a rule of thumb, instances of structural reuse, or focusing on structural similarity, are likely attributable to physicochemical constraints, whereas sequence reuse, or focusing on sequence similarity, may be more indicative of evolutionary relationships. Both types of relationships have been studied and can provide meaningful insights to protein biophysics and evolution, which in turn can lead to better algorithms for protein search, annotation, and maybe even design.In broad strokes, studies of protein space vary in the entities they represent, the similarity measure comparing these entities, and the representation used. The entities can be, for example, protein chains, domains, supra-domains, or smaller protein sub-parts denoted themes. The measures of similarity between the entities can be based on sequence, structure, function, or any combination of these. The representation can be global, encompassing the whole space, or local, focusing on a particular region surrounding protein(s) of interest. Global representations include lists of grouped proteins, protein networks, and maps. Networks are the abstraction that is derived most directly from the similarity data: each node is the protein entity (e.g., a domain), and edges connect similar domains. Selecting the entities, the similarity measure, and the abstraction are three intertwined decisions: the similarity measures allow us to identify the entities, and the selection of entities influences what is a meaningful similarity measure. Similarly, we seek entities that are related to each other in a way, for which a simple representation describes their relationships succinctly and accurately. This chapter will cover studies that rely on different entities, similarity measures, and a range of representations to better understand protein structure space. Scholars may use publicly available navigators offering a global representation, and in particular the hierarchical classifications SCOP, CATH, and ECOD, or a local representation, which encompass structural alignment algorithms. Alternatively, scholars can configure their own navigator using existing tools. To demonstrate this DIY (do it yourself) approach for navigating in protein space, we investigate substrate-binding proteins. By presenting sequence similarities among this large and diverse protein family as a network, we can infer that one member (pdb ID 4ntl; of yet unknown function) may bind methionine and suggest a putative binding mechanism.

Chang'E-5T Orbit Determination Using Onboard GPS Observations.

In recent years, Global Navigation Satellite System (GNSS) has played an important role in Space Service Volume, the region enclosing the altitudes above 3000 km up to 36,000 km. As an in-flight test for the feasibility as well as for the performance of GNSS-based satellite orbit determination (OD), the Chinese experimental lunar mission Chang'E-5T had been equipped with an onboard high-sensitivity GNSS receiver with GPS and GLONASS tracking capability. In this contribution, the 2-h onboard GPS data are evaluated in terms of tracking performance as well as observation quality. It is indicated that the onboard receiver can track 7-8 GPS satellites per epoch on average and the ratio of carrier to noise spectral density (C/N0) values are higher than 28 dB-Hz for 90% of all the observables. The C1 code errors are generally about 4.15 m but can be better than 2 m with C/N0 values over 36 dB-Hz. GPS-based Chang'E-5T OD is performed and the Helmert variance component estimation method is investigated to determine the weights of code and carrier phase observations. The results reveal that the orbit consistency is about 20 m. OD is furthermore analyzed with GPS data screened out according to different C/N0 thresholds. It is indicated that for the Chang'E-5T, the precision of OD is dominated by the number of observed satellite. Although increased C/N0 thresholds can improve the overall data quality, the available number of GPS observations is greatly reduced and the resulting orbit solution is poor.

Standalone GPS L1 C/A Receiver for Lunar Missions.

Global Navigation Satellite Systems (GNSSs) were originally introduced to provide positioning and timing services for terrestrial Earth users. However, space users increasingly rely on GNSS for spacecraft navigation and other science applications at several different altitudes from the Earth surface, in Low Earth Orbit (LEO), Medium Earth Orbit (MEO), Geostationary Earth Orbit (GEO), and feasibility studies have proved that GNSS signals can even be tracked at Moon altitude. Despite this, space remains a challenging operational environment, particularly on the way from the Earth to the Moon, characterized by weaker signals with wider gain variability, larger dynamic ranges resulting in higher Doppler and Doppler rates and critically low satellite signal availability. Following our previous studies, this paper describes the proof of concept "WeakHEO" receiver; a GPS L1 C/A receiver we developed in our laboratory specifically for lunar missions. The paper also assesses the performance of the receiver in two representative portions of an Earth Moon Transfer Orbit (MTO). The receiver was connected to our GNSS Spirent simulator in order to collect real-time hardware-in-the-loop observations, and then processed by the navigation module. This demonstrates the feasibility, using current technology, of effectively exploiting GNSS signals for navigation in a MTO.