Review of advanced guidance and control algorithms for space/aerospace vehicles

The design of advanced guidance and control (G&C) systems for space/aerospace vehicles has received a large amount of attention worldwide during the last few decades and will continue to be a main focus of the aerospace industry. Not surprisingly, due to the existence of various model uncertainties and environmental disturbances, robust and stochastic control-based methods have played a key role in G&C system design, and numerous effective algorithms have been successfully constructed to guide and steer the motion of space/aerospace vehicles. Apart from these stability theory-oriented techniques, in recent years, we have witnessed a growing trend of designing optimisation theory-based and artificial intelligence (AI)-based controllers for space/aerospace vehicles to meet the growing demand for better system performance. Related studies have shown that these newly developed strategies can bring many benefits from an application point of view, and they may be considered to drive the onboard decision-making system. In this paper, we provide a systematic survey of state-of-the-art algorithms that are capable of generating reliable guidance and control commands for space/aerospace vehicles. The paper first provides a brief overview of space/aerospace vehicle guidance and control problems. Following that, a broad collection of academic works concerning stability theory-based G&C methods is discussed. Some potential issues and challenges inherent in these methods are reviewed and discussed. Then, an overview is given of various recently developed optimisation theory-based methods that have the ability to produce optimal guidance and control commands, including dynamic programming-based methods, model predictive control-based methods, and other enhanced versions. The key aspects of applying these approaches, such as their main advantages and inherent challenges, are also discussed. Subsequently, a particular focus is given to recent attempts to explore the possible uses of AI techniques in connection with the optimal control of the vehicle systems. The highlights of the discussion illustrate how space/aerospace vehicle control problems may benefit from these AI models. Finally, some practical implementation considerations, together with a number of future research topics, are summarised

Novel genetic loci associated with hippocampal volume

The hippocampal formation is a brain structure integrally involved in episodic memory, spatial navigation, cognition and stress responsiveness. Structural abnormalities in hippocampal volume and shape are found in several common neuropsychiatric disorders. To identify the genetic underpinnings of hippocampal structure here we perform a genome-wide association study (GWAS) of 33,536 individuals and discover six independent loci significantly associated with hippocampal volume, four of them novel. Of the novel loci, three lie within genes (ASTN2, DPP4 and MAST4) and one is found 200 kb upstream of SHH. A hippocampal subfield analysis shows that a locus within the MSRB3 gene shows evidence of a localized effect along the dentate gyrus, subiculum, CA1 and fissure. Further, we show that genetic variants associated with decreased hippocampal volume are also associated with increased risk for Alzheimer's disease (rg =-0.155). Our findings suggest novel biological pathways through which human genetic variation influences hippocampal volume and risk for neuropsychiatric illness

SAGE-GCN: Graph Convolutional Network Based on Self-adaptive Stable Gates for Link Prediction in Dynamic Complex Networks

Link prediction is one of the most important tasks in uncovering evolving mechanisms of dynamic complex networks. Existing dynamic link prediction models suffer from limitations such as vulnerability to adversarial attacks, poor accuracy, and instability. In this paper, we propose a novel dynamic Graph Convolutional Network model incorporating a Self-adaptive Stable Gate (SAGE-GCN) consisting of a state encoding network and a policy network. Firstly, we capture the local topology of the nodes by employing a multi-power adjacency matrix to obtain higher-order topological features, enabling its features to be distinguished at different network snapshots. Then, a stable gate is introduced to ensure multiple spatiotemporal dependency paths within the state encoding network. It is proven that SAGE-GCN with integral Lipschitz graph convolution is stable to relative perturbations in the dynamic networks. Finally, a self-adaptive strategy is proposed to choose different state encoding network instances, with a policy network used to learn the optimal temporal and structural features through corresponding rewards to capture network dynamics. With the aid of extensive experiments on five real-world graph benchmarks, SAGE-GCN is shown to substantially outperform current state-of-the-art approaches in terms of precision and stability of dynamic link prediction and ability to successfully defend against various attacks.</p

Transfer Clustering Ensemble Selection

International audienceClustering ensemble (CE) takes multiple clusteringsolutions into consideration in order to effectively improve theaccuracy and robustness of the final result. To reduce redundancyas well as noise, a CE selection (CES) step is added to furtherenhance performance. Quality and diversity are two importantmetrics of CES. However, most of the CES strategies adoptheuristic selection methods or a threshold parameter setting toachieve tradeoff between quality and diversity. In this paper, wepropose a transfer CES (TCES) algorithm which makes use of therelationship between quality and diversity in a source dataset, andtransfers it into a target dataset based on three objective functions.Furthermore, a multiobjective self-evolutionary process isdesigned to optimize these three objective functions. Finally, weconstruct a transfer CE framework (TCE-TCES) based on TCESto obtain better clustering results. The experimental results on 12transfer clustering tasks obtained from the 20newsgroups datasetshow that TCE-TCES can find a better tradeoff between qualityand diversity, as well as obtaining more desirable clusteringresults

Cohort profile: Mental Health Living Longer: A population-wide data linkage to understand and reduce premature mortality in mental health service users in New South Wales, Australia

Purpose Health systems must move from recognition to action if we are to address premature mortality in people with mental illness. Population data registers are an essential tool for planning and monitoring improvement efforts. The Mental Health Living Longer (MHLL) programme establishes a population-wide data linkage to support research translation and service reform in New South Wales (NSW), Australia. Participants A total of 8.6 million people who have had contact with NSW public and private health services between July 2001 and June 2018 are currently included in the study. Data include more than 120 million linked records from NSW data collections covering public and private hospital care, emergency departments, ambulance, community mental health services, cancer notifications and care, and death registrations. Linkage is occurring with population-wide breast and cervical cancer screening programmes. Data will be updated 6 monthly. Findings to date The cohort includes 970 145 people who have received mental healthcare: 79% have received community mental healthcare, 35% a general hospital admission with a primary mental health diagnosis and 25% have received specialist mental health inpatient care. The most frequent pattern of care is receipt of community mental healthcare only (50%). The median age of the mental health cohort is 34 years, and three-quarters are younger than 53 years. Eleven per cent of the mental health cohort had died during the observation period. Their median age at death was 69 years, which was younger than the median age at death for people accessing other health services. Future plans The MHLL programme will examine (i) all-cause mortality, (ii) suicide, (iii) cancer mortality and (iv) medical mortality. Within each theme, the programme will quantify the problem in mental health service users compared with the NSW population, describe the people most affected, describe the care received, identify predictors of premature mortality, and identify variation and opportunities for change