Learning-based Nonlinear Model Predictive Control

© 2017 This paper presents stabilizing Model Predictive Controllers (MPC) in which prediction models are inferred from experimental data of the inputs and outputs of the plant. Using a nonparametric machine learning technique called LACKI, the estimated (possibly nonlinear) model function together with an estimation of Holder constant is provided. Based on these, a number of predictive controllers with stability guaranteed by design are proposed. Firstly, the case when the prediction model is estimated offline is considered and robust stability and recursive feasibility is ensured by using tightened constraints in the optimisation problem. This controller has been extended to the more interesting and complex case: the online learning of the model, where the new data collected from feedback is added to enhance the prediction model. An on-line learning MPC based on a double sequence of predictions is proposed.Spanish MINECO Grant PRX15-00300 and projects DPI2013-48243-C2-2-R and DPI2016-76493-C3-1-R. UK Engineering and Physical Research Council, grant no.EP/J012300/1

<i>Switching Go</i>̅<i>‑Martini</i> for Investigating Protein Conformational Transitions and Associated Protein–Lipid Interactions

Proteins are dynamic biomolecules that can transform between different conformational states when exerting physiological functions, which is difficult to simulate using all-atom methods. Coarse-grained (CG) Go̅-like models are widely used to investigate large-scale conformational transitions, which usually adopt implicit solvent models and therefore cannot explicitly capture the interaction between proteins and surrounding molecules, such as water and lipid molecules. Here, we present a new method, named Switching Go̅-Martini, to simulate large-scale protein conformational transitions between different states, based on the switching Go̅ method and the CG Martini 3 force field. The method is straightforward and efficient, as demonstrated by the benchmarking applications for multiple protein systems, including glutamine binding protein (GlnBP), adenylate kinase (AdK), and β2-adrenergic receptor (β2AR). Moreover, by employing the Switching Go̅-Martini method, we can not only unveil the conformational transition from the E2Pi-PL state to E1 state of the type 4 P-type ATPase (P4-ATPase) flippase ATP8A1-CDC50 but also provide insights into the intricate details of lipid transport

<i>Switching Go</i>̅<i>‑Martini</i> for Investigating Protein Conformational Transitions and Associated Protein–Lipid Interactions

Proteins are dynamic biomolecules that can transform between different conformational states when exerting physiological functions, which is difficult to simulate using all-atom methods. Coarse-grained (CG) Go̅-like models are widely used to investigate large-scale conformational transitions, which usually adopt implicit solvent models and therefore cannot explicitly capture the interaction between proteins and surrounding molecules, such as water and lipid molecules. Here, we present a new method, named Switching Go̅-Martini, to simulate large-scale protein conformational transitions between different states, based on the switching Go̅ method and the CG Martini 3 force field. The method is straightforward and efficient, as demonstrated by the benchmarking applications for multiple protein systems, including glutamine binding protein (GlnBP), adenylate kinase (AdK), and β2-adrenergic receptor (β2AR). Moreover, by employing the Switching Go̅-Martini method, we can not only unveil the conformational transition from the E2Pi-PL state to E1 state of the type 4 P-type ATPase (P4-ATPase) flippase ATP8A1-CDC50 but also provide insights into the intricate details of lipid transport

<i>Switching Go</i>̅<i>‑Martini</i> for Investigating Protein Conformational Transitions and Associated Protein–Lipid Interactions

Proteins are dynamic biomolecules that can transform between different conformational states when exerting physiological functions, which is difficult to simulate using all-atom methods. Coarse-grained (CG) Go̅-like models are widely used to investigate large-scale conformational transitions, which usually adopt implicit solvent models and therefore cannot explicitly capture the interaction between proteins and surrounding molecules, such as water and lipid molecules. Here, we present a new method, named Switching Go̅-Martini, to simulate large-scale protein conformational transitions between different states, based on the switching Go̅ method and the CG Martini 3 force field. The method is straightforward and efficient, as demonstrated by the benchmarking applications for multiple protein systems, including glutamine binding protein (GlnBP), adenylate kinase (AdK), and β2-adrenergic receptor (β2AR). Moreover, by employing the Switching Go̅-Martini method, we can not only unveil the conformational transition from the E2Pi-PL state to E1 state of the type 4 P-type ATPase (P4-ATPase) flippase ATP8A1-CDC50 but also provide insights into the intricate details of lipid transport

Recent Advances in Transition-Metal-Catalyzed/Mediated Transformations of Vinylidenecyclopropanes

ConspectusVinylidenecyclopropanes (VDCPs), having an allene moiety connected to a highly strained cyclopropyl group, have attracted a substantial amount of attention since they are fascinating building blocks for organic synthesis. During recent years, the reactions of VDCPs in the presence of a Lewis acid or a Brønsted acid and those induced by heat or light have experienced significant advancements due to the unique structural and electronic properties of VDCPs. Transition-metal-catalyzed reactions of VDCPs were not intensely investigated until the last 5 years. Recently, significant progress has been made in transition-metal-catalyzed transformations of VDCPs, and they have emerged as a new direction for the chemistry of strained small rings, especially when new types of functionalized vinylidenecyclopropanes (FVDCPs) are used as substrates. To date, many interesting transformations have been explored using these novel VDCPs under the catalysis of transition metals, such as gold, palladium, or rhodium, and various novel and useful heterocyclic or polycyclic compounds have been generated. These new findings have enriched the chemistry of strained small carbocycles.This Account will describe the transition-metal-catalyzed transformations of VDCPs recently developed in our laboratory and by other groups. The chemistry of Au-catalyzed VDCPs has been enriched and extensively developed by our group. In this respect, a new process for generating gold carbenes from VDCPs has been disclosed. The reactivity of these new gold carbenoid species was fully investigated, and many novel reaction modes based on these new gold carbenoid species were explored, including oxidation reactions, intramolecular cyclopropanations, C­(sp³)–H bond functionalizations, and C–O bond cleavage reactions. Rh-catalyzed reactions of VDCPs are another key field of transition-metal-catalyzed reactions of VDCPs. In particular, rhodium-catalyzed cycloadditions, Pauson–Khand reactions, and C–H bond activations of FVDCPs have been explored in detail by our group. A new trimethylenemethane rhodium (TMM–Rh) complex generated from VDCPs was discovered and utilized as an electrophilic Rh−π-allyl precursor. Moreover, some unprecedented highly regio- and enantioselective asymmetric allylic substitutions via this novel TMM–Rh complex were developed with different kinds of nucleophiles. This Account will also summarize the recent advances in palladium-, copper-, and iron-catalyzed cycloisomerization reactions of VDCPs reported by our group and others. These reactions always afford the desired products with excellent chemo-, regio-, diastereo-, and enantioselectivities, which will make them highly valuable for the synthesis of key scaffolds in natural products and pharmaceutical molecules in the future

Additional file 1 of Heterologous production of 3-hydroxypropionic acid in Methylorubrum extorquens by introducing the mcr gene via a multi-round chromosomal integration system based on cre-lox71/lox66 and transposon

Supplementary Material

Direct Conversion of Sugars and Ethyl Levulinate into γ‑Valerolactone with Superparamagnetic Acid–Base Bifunctional ZrFeO_<i>x</i> Nanocatalysts

Acid–base bifunctional superparamagnetic FeZrO_<i>x</i> nanoparticles were synthesized via a two-step process of solvothermal treatment and hydrolysis–condensation, and were further employed to catalyze the conversion of ethyl levulinate (EL) to γ-valerolactone (GVL) using ethanol as both H-donor and solvent. ZrFeO(1:3)-300 nanoparticles (12.7 nm) with Fe₃O₄ core covered by ZrO₂ layer (0.65 nm thickness) having well-distributed acid–base sites (0.39 vs 0.28 mmol/g), moderate surface area (181 m²/g), pore size (9.8 nm), and strong magnetism (35.4 Am² kg^–1) exhibited superior catalytic performance, giving a high GVL yield of 87.2% at 230 °C in 3 h. The combination of the nanoparticles with solid acid HY2.6 promoted the direct transformation of sugars to produce GVL in moderate yield (around 45%). Moreover, the nanocatalyst was easily recovered by a magnet for six cycles with an average GVL yield of 83.9% from EL

Training loss descent process.

Deep foundation pit settlement prediction based on machine learning is widely used for ensuring the safety of construction, but previous studies are limited to not fully considering the spatial correlation between monitoring points. This paper proposes a transformer-based deep learning method that considers both the spatial and temporal correlations among excavation monitoring points. The proposed method creates a dataset that collects all excavation monitoring points into a vector to consider all spatial correlations among monitoring points. The deep learning method is based on the transformer, which can handle the temporal correlations and spatial correlations. To verify the model’s accuracy, it was compared with an LSTM network and an RNN-LSTM hybrid model that only considers temporal correlations without considering spatial correlations, and quantitatively compared with previous research results. Experimental results show that the proposed method can predict excavation deformations more accurately. The main conclusions are that the spatial correlation and the transformer-based method are significant factors in excavation deformation prediction, leading to more accurate prediction results.</div

Fitted creep curves of backfill materials with different moisture contents [33].

Fitted creep curves of backfill materials with different moisture contents [33].</p

Fig 9 -

Prediction result distributed over observation points: (a) time 1; (b) time 2.</p