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Our goal is to recover time-delayed latent causal variables and identify their relations from measured temporal data. Estimating causally-related latent variables from observations is particularly challenging as the latent variables are not uniquely recoverable in the most general case. In this work, we consider both a nonparametric, nonstationary setting and a parametric setting for the latent processes and propose two provable conditions under which temporally causal latent processes can be identified from their nonlinear mixtures. We propose LEAP, a theoretically-grounded architecture that extends Variational Autoencoders (VAEs) by enforcing our conditions through proper constraints in causal process prior. Experimental results on various data sets demonstrate that temporally causal latent processes are reliably identified from observed variables under different dependency structures and that our approach considerably outperforms baselines that do not leverage history or nonstationarity information. This is one of the first works that successfully recover time-delayed latent processes from nonlinear mixtures without using sparsity or minimality assumptions.

Inevitable safety issues have pushed battery engineers to become more conservative in battery system design; however, battery‐involved accidents still frequently are reported in headlines. Identifying, understanding, and predicting safety risks have become priorities to further accelerate technology and industry development. However, diverse loading scenarios, significantly varied stress‐induced short circuit mechanisms, and highly coupled mechanical–electrochemical safety behaviors have remained grand challenges. Herein, the safety risk is termed as the probability of the mechanical triggering of an internal short circuit, to reflect the safety related behaviors of lithium‐ion batteries. Based on a mechanical model and experimental results, a sufficient dataset is generated consisting of strain states and their corresponding safety risks, covering both cylindrical and pouch cells, various states of charges, and loading conditions. Machine‐learning tools combined with the established finite element mechanical model are applied to predict the safety risks of the cells. The results achieve a high level of accuracy on the test data (the relative error of the average short circuit prediction deviation is less than 6.2%.). This work underpins the safety risk concept and highlights the promise of physics combined with data‐driven modeling methodology to predict the safety behaviors of energy storage systems.

We isolated an aromatic strain of yeast (M2013310) from chili sauce. Assembly, annotation, and phylogenetic analysis based on genome sequencing, identified M2013310 as an allodiploid yeast that was closely related to Zygosaccharomyces rouxii . During fermentation, M2013310, produced an aromatic alcohol with a rose-honey scent; gas chromatography tandem mass spectrometry identified this alcohol as 2-phenylethanol. The concentration of 2-phenylethanol reached 3.8 mg/L, 1.79 g/L, and 3.58 g/L, in M3 (NH 4 + ), M3 (NH 4 + + Phe), and M3 (Phe) culture media, after 72 h of fermentation, respectively. The mRNA expression levels of ARO8 encoding aromatic aminotransferases I and ARO10 encoding phenylpyruvate decarboxylase by M2013310 in M3 (Phe) were the lowest of the three different forms of media tested. These results indicated that M2013310 can synthesize 2-phenylethanol via the Shikimate or Ehrlich pathways and the production of 2-phenylethanol may be significantly improved by the over-expression of these two genes. Our research identified a promising strain of yeast (M2013310) that could be used to improve the production of 2-phenylethanol.

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External perturbations and actuator faults are two practical and significant issues that deserve designers' considerations when synthesizing the controllers for spacecraft rendezvous. A composite robust fault-tolerant control (FTC) scheme that does not require the fault information is proposed in this paper for limited-thrust rendezvous in near-circular orbits. Within the control scheme, a reliable integral sliding mode (ISM) auxiliary controller and a modified guaranteed cost FTC are, respectively, developed to attenuate the external disturbances and to stabilize the nominal rendezvous system with actuator faults. Comparisons with previous works as well as a more practical and challenging simulation example are presented to verify the advantages of this composite control scheme.

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A triple-stage path prediction algorithm is proposed for real-time mission planning. In every planning horizon, each unmanned aerial vehicle (UAV) utilises the A* algorithm first to estimate the path to every task. Then, the estimated result serve as the input of the cluster method to generate the quasi-optimal task assignment. The shortest path to the assigned task is further calculated using the A* algorithm. Finally, it is smoothed to obtain the flyable reference path to guide the UAV by using the cubic B-spline curve. Numerical experiments demonstrate the effectiveness and efficiency of the proposed algorithm.