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Aims Design Methods Results Conclusion Impact Statement Patient or Public Contribution To analyse the current status of psychological resilience in Parkinson's disease (PD) patients and its correlation with social support and coping style.A cross‐sectional study.PD patients hospitalized in a tertiary‐level hospital in Shijiazhuang, Hebei Province, from March 2022 to March 2023 were selected for the study using the convenience sampling method. A general information questionnaire, psychological resilience scale, Medical Coping Modes Questionnaire and Perceived Social Support Scale were used to investigate 111 cases of PD. SPSS 25.0 software was used for statistical analysis. The data were analysed using independent samples t‐test, one‐way ANOVA, multiple linear regression analysis and the Pearson correlation coefficient.Parkinson's disease patients have a moderate level of psychological resilience. The results of the Pearson correlation analyses showed that the level of psychological resilience was positively correlated with social support and confrontation and was negatively correlated with avoidance and acceptance‐resignation. The results of multiple linear regression analysis showed that social support and acceptance‐resignation were the influencing factors of psychological resilience in PD patients.The psychological resilience of PD patients is at a moderate level. Social support and acceptance‐resignation are the factors influencing the psychological resilience of PD patients.This study analysed the level of psychological resilience in PD patients and its correlation with social support and coping style from the perspective of positive psychology to provide some reference for targeted clinical interventions. Our study found that social support and acceptance‐resignation are influential factors in psychological resilience in PD patients. Medical staff should encourage patients to face the disease positively and their social support should be increased in order to improve their level of psychological resilience.No patient or public contribution. [ABSTRACT FROM AUTHOR]

This work develops a new robust multi‐objective model predictive control (MoMPC) strategy for constrained non‐linear systems with bounded disturbances. The multiple objectives are always contradictory, and the presence of disturbances may result in the violation of state constraints and instability of the controlled system. Firstly, the conflict between multiple objectives is reconciled by minimizing the distance of the cost function vector to the vector of independently minimised objectives obtained by solving a set of steady‐state optimisation problems. Then the contractive state set, which takes into account the effect of disturbances on system states, is constructed to guarantee the robust satisfaction of state constraints. Finally, a stability constraint updated online by solving an auxiliary optimization problem is established to ensure the robust stability of the system under MoMPC. An isothermal chemical reactor system is employed to verify the effectiveness of the controller proposed here. [ABSTRACT FROM AUTHOR]

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Crop models are widely used to assist in agricultural management decision-making and water productivity optimization. However, traditional crop models often depend on artificial and specific field management inputs, posing challenges in maintaining crops within a desired stress range. Consequently, the derived optimization schemes from these models become highly uncertain. Moreover, the complexity of the mechanisms involved and the multitude of parameters make it challenging to apply traditional crop models uniformly across various crops and regions. In this study, we have developed a novel crop model called WHCrop (Water-Heat Driven Crop model) that effectively captures, reflects, and controls the impact of various environmental factors (meteorology, topography, soil, and management) on crop growth process. The WHCrop model combines the simulation principles of biomass and yield from the CERES module in the DSSAT model, along with the soil water balance from the AquaCrop model, to estimate the dynamics of crop growth and production processes. Results indicated that WHCrop-based simulations, including canopy cover (CC), daily evapotranspiration (ET), and yield, matched well with ground-based measurements, and were better than the traditional crop model (DSSAT and AquaCrop) at both field and regional scales, especially under deficient irrigation conditions. Besides capturing the key variables associated with crop growth, WHCrop model could reproduce the adaptive response of these various to regional-scale temperature changes. Notably, the WHCrop model could effectively minimize uncertainties resulting from individual environmental change, thanks its incorporation of dynamic response mechanisms for crop growth under stress factors. Overall, the novel and informative WHCrop model offers some advantages over traditional crop models since it allows for optimal decision making to be derived from the randomly different inputs. As a result, the WHCrop model proves instrumental in assisting decision-makers in formulating critical water allocation strategies and developing effective management recommendations to enhance regional agricultural water productivity. • WHCrop model simulates crop growth dynamics using water and heat drive. • WHCrop model enables randomly varied inputs for optimal decision-making. • WHCrop model simulates daily crop growth responses to water stress and climate changes. • WHCrop model needs fewer and readily available input data than traditional crop model. • WHCrop outperformed traditional crop models in simulating crop growth across scales. [ABSTRACT FROM AUTHOR]