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Klebsiella pneumoniae continues to be a substantial public health threat due to its ability to cause health care-associated and community-acquired infections combined with its ability to acquire antibiotic resistance. Novel therapeutics are needed to combat this pathogen, and a greater understanding of its virulence factors is required for the development of new drugs. A key virulence factor for K. pneumoniae is the capsule, and community-acquired hypervirulent strains produce a capsule that causes hypermucoidy. We report here a novel capsule regulator, RmpC, and provide evidence that capsule production and the hypermucoviscosity phenotype are distinct processes. Infection studies showing that this and other capsule regulator mutants have a range of phenotypes indicate that additional virulence factors are in their regulons. These results shed new light on the mechanisms controlling capsule production and introduce targets that may prove useful for the development of novel therapeutics for the treatment of this increasingly problematic pathogen. The polysaccharide capsule is an essential virulence factor for Klebsiella pneumoniae in both community-acquired hypervirulent strains as well as health care-associated classical strains that are posing significant challenges due to multidrug resistance. Capsule production is known to be transcriptionally regulated by a number of proteins, but very little is known about how these proteins collectively control capsule production. RmpA and RcsB are two known regulators of capsule gene expression, and RmpA is required for the hypermucoviscous (HMV) phenotype in hypervirulent K. pneumoniae strains. In this report, we confirmed that these regulators performed their anticipated functions in the ATCC 43816 derivative, KPPR1S: rcsB and rmpA mutants are HMV negative and have reduced capsule gene expression. We also identified a novel transcriptional regulator, RmpC, encoded by a gene near rmpA . The Δ rmpC strain has reduced capsule gene expression but retains the HMV phenotype. We further showed that a regulatory cascade exists in which KvrA and KvrB, the recently characterized MarR-like regulators, and RcsB contribute to capsule regulation through regulation of the rmpA promoter and through additional mechanisms. In a murine pneumonia model, the regulator mutants have a range of colonization defects, suggesting that they regulate virulence factors in addition to capsule. Further testing of the rmpC and rmpA mutants revealed that they have distinct and overlapping functions and provide evidence that HMV is not dependent on overproduction of capsule. This distinction will facilitate a better understanding of HMV and how it contributes to enhanced virulence of hypervirulent strains. IMPORTANCE Klebsiella pneumoniae continues to be a substantial public health threat due to its ability to cause health care-associated and community-acquired infections combined with its ability to acquire antibiotic resistance. Novel therapeutics are needed to combat this pathogen, and a greater understanding of its virulence factors is required for the development of new drugs. A key virulence factor for K. pneumoniae is the capsule, and community-acquired hypervirulent strains produce a capsule that causes hypermucoidy. We report here a novel capsule regulator, RmpC, and provide evidence that capsule production and the hypermucoviscosity phenotype are distinct processes. Infection studies showing that this and other capsule regulator mutants have a range of phenotypes indicate that additional virulence factors are in their regulons. These results shed new light on the mechanisms controlling capsule production and introduce targets that may prove useful for the development of novel therapeutics for the treatment of this increasingly problematic pathogen.

Capsule is a critical virulence factor in Klebsiella pneumoniae , in both antibiotic-resistant classical strains and hypervirulent strains. Hypervirulent strains usually have a hypermucoviscosity (HMV) phenotype that contributes to their heightened virulence capacity, but the production of HMV is not understood. The transcriptional regulator RmpA is required for HMV and also activates capsule gene expression, leading to the assumption that HMV is caused by hyperproduction of capsule. We have identified a new gene ( rmpD ) required for HMV but not for capsule production. This distinction between HMV and capsule production will promote a better understanding of the mechanisms of hypervirulence, which is in great need given the alarming increase in clinical isolates with both drug resistance and hypervirulence traits. Klebsiella pneumoniae has a remarkable ability to cause a wide range of human diseases. It is divided into two broad classes: classical strains that are a notable problem in health care settings due to multidrug resistance, and hypervirulent (hv) strains that are historically drug sensitive but able to establish disease in immunocompetent hosts. Alarmingly, there has been an increased frequency of clinical isolates that have both drug resistance and hv-associated genes. One such gene, rmpA , encodes a transcriptional regulator required for maximal capsule ( cps ) gene expression and confers hypermucoviscosity (HMV). This link has resulted in the assumption that HMV is caused by elevated capsule production. However, we recently reported a new cps regulator, RmpC, and Δ rmpC mutants have reduced cps expression but retain HMV, suggesting that capsule production and HMV may be separable traits. Here, we report the identification of a small protein, RmpD, that is essential for HMV but does not impact capsule. RmpD is 58 residues with a putative N-terminal transmembrane domain and highly positively charged C-terminal half, and it is conserved among other hv K. pneumoniae strains. Expression of rmpD in trans complements both Δ rmpD and Δ rmpA mutants for HMV, suggesting that RmpD is the key driver of this phenotype. The rmpD gene is located between rmpA and rmpC , within an operon regulated by RmpA. These data, combined with our previous work, suggest a model in which the RmpA-associated phenotypes are largely due to RmpA activating the expression of rmpD to produce HMV and rmpC to stimulate cps expression. IMPORTANCE Capsule is a critical virulence factor in Klebsiella pneumoniae , in both antibiotic-resistant classical strains and hypervirulent strains. Hypervirulent strains usually have a hypermucoviscosity (HMV) phenotype that contributes to their heightened virulence capacity, but the production of HMV is not understood. The transcriptional regulator RmpA is required for HMV and also activates capsule gene expression, leading to the assumption that HMV is caused by hyperproduction of capsule. We have identified a new gene ( rmpD ) required for HMV but not for capsule production. This distinction between HMV and capsule production will promote a better understanding of the mechanisms of hypervirulence, which is in great need given the alarming increase in clinical isolates with both drug resistance and hypervirulence traits.

This study investigates the integration of Robust Model Predictive Control (RMPC) and Deep Learning to enhance the performance and adaptability of energy systems, focusing on Combined Heat and Power (CHP), Power-to-Hydrogen, and Power-to-Gas Methane applications. The proposed framework combines RMPC’s robustness with Deep Learning’s ability to learn and adapt, improving control precision and operational efficiency. Extensive simulations indicate that the integrated RMPC-Deep Learning system improves control accuracy by 8.02% compared to conventional methods, while also reducing energy consumption by 12.14%. These quantitative results demonstrate the effectiveness of the proposed system in addressing challenges such as operator saturation, showcasing its potential to optimize energy systems under dynamic conditions. This work highlights the transformative role of merging RMPC with Deep Learning, providing a robust and adaptable solution for energy management in complex applications.

This paper addresses the challenge of renewable energy curtailment, which stems from the inherent uncertainty and volatility of wind and photovoltaic (PV) generation, by developing a robust model predictive control (RMPC)-based scheduling strategy for an integrated wind–PV–hydrogen storage multi-energy flow system. By building a “wind–PV–hydrogen storage–fuel cell” collaborative system, the time and space complementarity of wind and PV is used to stabilize fluctuations, and the electrolyzer–hydrogen production–gas storage tank–fuel cell chain is used to absorb surplus power. A multi-time scale state-space model (SSM) including power balance equation, equipment constraints, and opportunity constraints is established. The RMPC scheduling framework is designed, taking the wind–PV joint probability scene generated by Copula and improved K-means and SSM state variables as inputs, and the improved genetic algorithm is used to solve the min–max robust optimization problem to achieve closed-loop control. Validation using real-world data from Xinjiang demonstrates a 57.83% reduction in grid power fluctuations under extreme conditions and a 58.41% decrease in renewable curtailment rates, markedly enhancing the local system’s capacity to utilize wind and solar energy.

This paper mainly studies the stability of a sea cucumber adsorption robot (SCAR) under external disturbances and parameter uncertainty. As a fishing robot with a suction effect, a dynamic model of its vertical operation is established, taking into account the mechanical structure of its suction port and the surrounding flow conditions. Specifically, robust model predictive control (RMPC) is adopted to ensure that the device maintains excellent stability in complex underwater environments. Considering the simplification of the model structure and real-time control, a discrete nominal model with the introduction of a feedback correction mechanism is designed to be close to the actual system. In the process of control design, the upper bound of robot speed and propeller saturation have been taken into account, and the optimization functions to reduce input consumption of the propellers are also provided at the same time. Furthermore, the feasibility of the recursive structure and the stability of the closed-loop structure are proved. Importantly, the external factors including disturbance sources obtained from pool experiments are subjected to our consideration. In total, the simulation results and comparative analysis are evidenced by the practicality of the designed vehicle and the effectiveness of the established methodology.

This study proposes a Tube-based Robust Model Predictive Control (Tube-RMPC) strategy for autonomous vehicle control to address model parameter uncertainties and variations in road–tire adhesion coefficients in complex road scenarios. More specifically, the proposed approach improves the representation of vehicle dynamic behavior by introducing a unified vehicle–tire modeling framework. To facilitate computational tractability and algorithmic implementation, the model is systematically linearized and discretized. Furthermore, the Tube-based Robust Model Predictive Control strategy is developed to improve adaptability to uncertainty in the road surface adhesion coefficient. The Tube-based Robust Model Predictive controller ensures robustness by establishing a robust invariant tube around the nominal trajectory, effectively mitigating road surface variations and enhancing stability. Finally, a co-simulation platform integrating CarSim and Simulink is employed to validate the proposed method’s effectiveness. The experimental results demonstrate that Tube-RMPC improves the path-tracking performance, reducing the maximum tracking error by up to 9.17% on an S-curve and 2.25% in a double lane change, while significantly lowering RMSE and enhancing yaw stability compared to MPC and PID.

Lead (Pb) is one of the most hazardous heavy-metal pollutants in the environment. However, the resistance of different soils and ecosystems to Pb pollution varies greatly. In the present study, the comparative assessment of the resistance to Pb contamination in the forest, forest-steppe, steppe, and mountain-meadow soils of the Central Ciscaucasia and the Caucasus regions was conducted. There were 10 types and subtypes of objects from the forest, forest-steppe, steppe, and mountain-meadow soils which were selected for this study. The laboratory modeling of the effect of chemical soil contamination with lead (II) oxide (PbO) at different concentrations, 100, 1000, and 10,000 mg/kg, were introduced into the soil to check the microbiological, biochemical, and phytotoxic properties of the soil after 30 days of incubation. Soil resistance to Pb stress was assessed by the degree of the decrease in the most sensitive and informative biological indicators of the soil condition. It has been found that the forest-steppe and steppe soils showed a greater resistance than that of the forest and mountain-meadow soils. The regional maximum permissible concentration (rMPC) of Pb was developed for the first time, according to the degree of violation of the ecosystem functions of the soils. The forecast maps were developed for the deterioration of the soil condition during the Pb contamination at variable concentrations in the Central Ciscaucasia and the Caucasus regions.

This article is concerned with the robust model predictive control (RMPC) problem for polytopic uncertain systems under the round-robin (RR) scheduling in the high-rate communication channel. From a set of sensors to the controller, several sensors transmit the data to the remote controller via a shared high-rate communication network, data collision might happen if these sensors start transmissions at the same time. For the sake of preventing data collision in the high-rate communication channel, a communication scheduling known as RR is used to arrange the data transmission order, where only one node with token is allowed to send data at each transmission instant. In accordance with the token-dependent Lyapunov-like approach, the aim of the problem addressed is to design a set of controllers in the framework of RMPC such that the asymptotical stability of the closed-loop system is guaranteed. By taking the effect of the underlying RR scheduling in the high-rate communication channel into consideration, sufficient conditions are obtained by solving a terminal constraint set of an auxiliary optimization problem. In addition, an algorithm including both off-line and online parts is provided to find a sub-optimal solution. Finally, two simulation examples are used to demonstrate the usefulness and effectiveness of the proposed RMPC strategy.

The present work is concerned with the recursive estimation of the uncertainty polytope in a robust model predictive control (RMPC) framework. For this purpose, the unknown but bounded error method is employed to update the uncertainty polytope on the basis of sensor measurements at each sampling period. The recursive feasibility and asymptotic stability properties of the proposed approach are demonstrated as an extension of previous results concerning the RMPC formulation. For illustration, a simulated example involving an angular positioning system is presented. The results show that the proposed scheme provides a performance improvement, as indicated by the resulting cost function values.

The aim of this paper is to find the non-linear behavior of a container ship roll motion by using fin-roll stabilizer and robust model predictive control (RMPC). To do so, numerical and analytical modeling has been introduced for the roll motion. Computational fluid dynamics method was employed to determine the hydrodynamic lift of the fin at various angles. RMPC was designed and used to control the non-linear roll motion in the presence of disturbances, uncertainties, which were caused by the irregular sea waves, and operational constraints of fin's actuator. To boost the validity of our results, the performance of this controller was compared with a conventional PID (Proportional-Integral-Derivative) controller. Simulation results indicated the significant amount of reduction in roll amplitude and roll rate.