Data-Driven Control of Stochastic Systems: An Innovation Estimation Approach

Recent years have witnessed a booming interest in the data-driven paradigm for predictive control. However, under noisy data ill-conditioned solutions could occur, causing inaccurate predictions and unexpected control behaviours. In this article, we explore a new route toward data-driven control of stochastic systems through active offline learning of innovation data, which gives an answer to the critical question of how to derive an optimal data-driven model from a noise-corrupted dataset. A generalization of the Willems' fundamental lemma is developed for non-parametric representation of input-output-innovation trajectories, provided realizations of innovation are precisely known. This yields a model-agnostic unbiased output predictor and paves the way for data-driven receding horizon control, whose behaviour is identical to the ``oracle" solution of certainty-equivalent model-based control with measurable states. For efficient innovation estimation, a new low-rank subspace identification algorithm is developed. Numerical simulations show that by actively learning innovation from input-output data, remarkable improvement can be made over present formulations, thereby offering a promising framework for data-driven control of stochastic systems

Accelerated Nonconvex ADMM with Self-Adaptive Penalty for Rank-Constrained Model Identification

The alternating direction method of multipliers (ADMM) has been widely adopted in low-rank approximation and low-order model identification tasks; however, the performance of nonconvex ADMM is highly reliant on the choice of penalty parameter. To accelerate ADMM for solving rankconstrained identification problems, this paper proposes a new self-adaptive strategy for automatic penalty update. Guided by first-order analysis of the increment of the augmented Lagrangian, the self-adaptive penalty updating enables effective and balanced minimization of both primal and dual residuals and thus ensures a stable convergence. Moreover, improved efficiency can be obtained within the Anderson acceleration scheme. Numerical examples show that the proposed strategy significantly accelerates the convergence of nonconvex ADMM while alleviating the critical reliance on tedious tuning of penalty parameters.Comment: 7 pages, 4 figures. Submitted to 62nd IEEE Conference on Decision and Control (CDC 2023

Genome-Wide Association Study on Root Traits Under Different Growing Environments in Wheat (Triticum aestivum L.)

Plant roots are critical for water and nutrient acquisition, environmental adaptation, and yield formation. Herein, 196 wheat accessions from the Huang-Huai Wheat Region of China were collected to investigate six root traits at seedling stage under three growing environments [indoor hydroponic culture (IHC), outdoor hydroponic culture (OHC), and outdoor pot culture (OPC)] and the root dry weight (RDW) under OPC at four growth stages and four yield traits in four environments. Additionally, a genome-wide association study was performed with a Wheat 660K SNP Array. The results showed that the root traits varied most under OPC, followed by those under both OHC and IHC, and root elongation under hydroponic culture was faster than that under pot culture. Root traits under OHC might help predict those under OPC. Moreover, root traits were significantly negatively correlated with grain yield (GY) and grains per spike (GPS), positively correlated with thousand-kernel weight (TKW), and weakly correlated with number of spikes per area (SPA). Twelve stable chromosomal regions associated with the root traits were detected on chromosomes 1D, 2A, 4A, 4B, 5B, 6D, and unmapped markers. Among them, a stable chromosomal interval from 737.85 to 742.00 Mb on chromosome 4A, which regulated total root length (TRL), was identified under three growing environments. Linkage disequilibrium (LD) blocks were used to identify 27 genes related to root development. Three genes TraesCS4A02G484200, TraesCS4A02G484800, TraesCS4A02G493800, and TraesCS4A02G493900, are involved in cell elongation and differentiation and expressed at high levels in root tissues. Another vital co-localization interval on chromosome 5B (397.72–410.88 Mb) was associated with not only RDW under OHC and OPC but also TKW

Convergence of the 26S proteasome and the REVOLUTA pathways in regulating inflorescence and floral meristem functions in Arabidopsis

The 26S proteasome is a large multisubunit proteolytic complex, regulating growth and development in eukaryotes by selective removal of short-lived regulatory proteins. Here, it is shown that the 26S proteasome and the transcription factor gene REVOLUTA (REV) act together in maintaining inflorescence and floral meristem (IM and FM) functions. The characterization of a newly identified Arabidopsis mutant, designated ae4 (asymmetric leaves1/2 enhancer4), which carries a mutation in the gene encoding the 26S proteasome subunit, RPN2a, is reported. ae4 and rev have minor defects in phyllotaxy structure and meristem initiation, respectively, whereas ae4 rev demonstrated strong developmental defects. Compared with the rev single mutant, an increased percentage of ae4 rev plants exhibited abnormal vegetative shoot apical and axillary meristems. After flowering, ae4 rev first gave rise to a few normal-looking flowers, and then flowers with reduced numbers of all types of floral organs. In late reproductive development, instead of flowers, the ae4 rev IM produced numerous filamentous structures, which contained cells seen only in the floral organs, and then carpelloid organs. In situ hybridization revealed that expression of the WUSCHEL and CLAVATA3 genes was severely down-regulated or absent in the late appearing ae4 rev primordia, but the genes were strongly expressed in top-layer cells of inflorescence tips. Double mutant plants combining rev with other 26S proteasome subunit mutants, rpn1a and rpn9a, resembled ae4 rev, suggesting that the 26S proteasome might act as a whole in regulating IM and FM functions

Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition)

In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. For example, a key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process versus those that measure fl ux through the autophagy pathway (i.e., the complete process including the amount and rate of cargo sequestered and degraded). In particular, a block in macroautophagy that results in autophagosome accumulation must be differentiated from stimuli that increase autophagic activity, defi ned as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (inmost higher eukaryotes and some protists such as Dictyostelium ) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the fi eld understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. It is worth emphasizing here that lysosomal digestion is a stage of autophagy and evaluating its competence is a crucial part of the evaluation of autophagic flux, or complete autophagy. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. Along these lines, because of the potential for pleiotropic effects due to blocking autophagy through genetic manipulation it is imperative to delete or knock down more than one autophagy-related gene. In addition, some individual Atg proteins, or groups of proteins, are involved in other cellular pathways so not all Atg proteins can be used as a specific marker for an autophagic process. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field

Alternative Formulations and Improvements with Valid Inequalities for the Refinery Production Scheduling Problem Involving Operational Transitions

Operation mode switching of production units would result in long transitions with fluctuant product yields, production costs, and key product properties. To formulate the remarkable process dynamics, a mixed-integer linear programming (MILP) model involving operational transitions of mode switching was proposed for the refinery production scheduling problem in Shi et al. (Ind. Eng. Chem. Res. 2014, 53 (19), 8155−8170), which can describe transitional behaviors and provide implementable schedules. However, the model is very computationally expensive because it involves a large number of discrete and continuous variables, and the constraints about operational transitions are numerous and complex. In this paper, we study a scheduling problem similar to that in Shi et al. (Ind. Eng. Chem. Res. 2014, 53 (19), 8155−8170) and aim at improving the computational efficiency of MILP models by alternative formulations and valid inequalities. First, we redefine some sets, variables and introduce new variables to propose the basic formulation, with which the nonlinear items are avoided and the variable definitions are simplified. Second, by reformulating constraints to explicitly describe the Fixed Charge Network Flow characteristic of operation mode switching, three Fixed-Charge-Network-Flow-based reformulations are proposed. Third, valid inequalities are developed for lot-sizing relaxations derived from the reformulations. Computational results show that the basic formulation is much smaller-sized and its computational performance is significantly better. The Fixed-Charge-Network-Flow-based reformulations together with the valid inequalities can further reduce by up to more than 95% the computational time while tightening the linear relaxation of the MILP model by more than 45%

Plant planning optimization under time-varying uncertainty: Case study on a Poly (vinyl chloride) plant

Planning optimization considering various uncertainties has attracted increasing attentions in the process industry. In the existing studies, the uncertainty is often described with a time-invariant distribution function during the entire planning horizon, which is a questionable assumption. Particularly, for long-term planning problems, the uncertainty tends to vary with time and it usually increases when a model is used to predict the parameter (e.g. price) far into the future. In this paper, time-varying uncertainties are considered in robust planning problems with a focus on a polyvinyl chloride (PVC) production planning problem. Using the stochastic programming techniques, a stochastic model is formulated, and then transformed into a multi-period mixed-integer linear programming (MILP) model by chance constrained programming and piecewise linear approximation. The proposed approach is demonstrated on industrial-scale cases originated from a real-world PVC plant. The comparisons show that the model considering varying-uncertainty is superior in terms of robustness under uncertainties

Effects of Aqua-Dispersing Nano-Binder on Clay Conductivity at Different Temperatures

Soil nutrients are the basis of ecological remediation. Soil amendments can form a reticular membrane structure on the soil surface to increase nutrient storage and alleviate nutrient imbalances, and are affected by the environmental temperature. At present, the qualitative evaluation of the effect of soil amendment is mainly based on vegetative growth. However, with the increasing use of soil amendments, how to conveniently and quantitatively evaluate the impact of soil amendments on ecological restoration under different temperature conditions from the perspective of soil urgently needs to be solved. Therefore, a new soil amendment named aqua-dispersing nano-binder (ADNB) and silty clay that is commonly used for ecological restoration in South China were used as research subjects, and the important soil nutrient storage capacity—soil conductivity index—was used as the starting point to find solutions to the above problems. We independently developed a multifunctional instrument to measure the soil amendment concentration. Clay conductivity measurements were used by adding different concentrations of ADNB within the range of 0 to 50 °C, and the mechanism by which temperature and ADNB affect the conductivity of clay was revealed. In addition, the quantitative relationship between the clay conductivity, ambient temperature and concentration of ADNB was elucidated. According to the growth conditions of melinis minutiflora and pigeon pea under different concentrations of ADNB, the optimal ADNB concentration needed to improve ecological restoration was obtained, which provided a new way to evaluate the effects of the large-scale use of soil modifiers on ecological restoration

Study on the Stabilization Mechanisms of Clayey Slope Surfaces Treated by Spraying with a New Soil Additive

The topsoil of a clayey slope is easily washed off by rain due to its loose structure. To protect the slope surface, in recent years, several types of non-traditional soil additives have been used by means of mixing with soil. In this work, a new organic polymer soil stabilizer, named aqua-dispersing-nano-binder (ADNB), was sprayed on the soil surface to stabilize the topsoil of a clayey slope. To understand the interaction between the polymer and soil particles during the infiltration process as well as the stabilization mechanism, infiltration tests, water stability tests and scanning electron microscopy (SEM) analyses were performed with different polymer contents. The infiltration tests showed that the infiltration rate of the polymer stabilizer in the soil was slower than that of water due to its characteristics of easy adhesion to soil particles, poor fluidity and large molecular volume. The maximum effective infiltration depth was achieved in the specimen treated with 2% ADNB, and the minimum was achieved in the specimen treated with 5% ADNB. The water stability of the soil increased with the content of the soil stabilizer in the soil aggregates with diameters of either 5–10 mm or 10–20 mm. The SEM analysis showed that the quantity of polymer decreased with infiltration depth; a polymer membrane was formed on the surface of the topsoil and chains were formed inside. The amelioration of the soil water stability may have been due to the bonding between soil particles and polymers generated after evaporation of water in the emulsion. The polymer stabilizer could be applied to improve the erosion resistance of the slope topsoil and reduce soil loss