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Background With the acceleration of the aging process in society, the demand for hospice services is increasing. The development of hospice care in China started relatively late, and relevant institutional construction standards and management norms are still in the trial stage. As one of the important integrated medical and nursing service institutions, elderly care institutions currently lack an effective and homogeneous model and service system for hospice services. Objective To establish a scientific, standardized and feasible hospice service model for elderly care institutions. Methods From April to May in 2020, Chinese/English databases were systematically searched to obtain the literature related to hospice services. Five experts in the field of hospice care were invited to carry out semi-structured interviews, and the initial index system was constructed based on the results of literature retrieval and semi-structured interviews. In May 2020, a purposive sampling method was used to select fifteen experts in the field of hospice care to conduct two rounds of correspondence, and the indicator system was determined based on the analysis of the research group and expert suggestions. After two rounds of correspondence, an expert group interview was conducted to further optimize and adjust the formed indicator system. Results In both rounds of correspondence, the questionnaire response rate was 100.0%, with expert authority coefficients of 0.89 and 0.94, respectively. The Kendall's W coefficients for the first level indicator were 0.54 and 0.59, respectively, and the Kendall's W coefficients for the second level indicator were 0.18 and 0.10, respectively. The final constructed indicator system for the hospice service model of elderly care institutions included three primary indicators of necessary resources and conditions, content and processes, assessment indicators, and 36 secondary indicators. In the second round of inquiry, the average importance score of the indicator was 3.87 to 5.00 points, the coefficient of variation was 0 to 0.25, and the full score rate was 63.0% to 100.0%. Conclusion The scientificity and reliability of the model hospice service model for elderly care institutions constructed in this study are good, which can provide reference for elderly care institutions to provide high-quality medical services for elderly patients at the end of their lives.

Temporal logic can ensure the accurate description and execution of task specifications by providing a rich specification language. This paper uses relevant robust semantics to consider continuous-time systems under signal temporal logic specification fragments and uses a funnel-based feedback control law to realize collaborative design of path planning and control of wheeled mobile robot. This design not only introduces a strict and effective formal verification method, but also provides a computationally-efficient funnel-based feedback control law. Firstly, it describes the mission by signal temporal logic. Then, it transforms the description into robust formula. Finally, using the funnel-based feedback control law, it makes the robust semantics of the robust formula maximum to realize the collaborative design of path planning and control. Simulation is based on the wheeled mobile robot kinematics model, and the results show that the collaborative design method proposed in this paper is effective. When the specification is physically met, the system can achieve the goal with user-defined robustness. When the specification is not satisfied, the minimum violation solution can be found. The proposed path planning and funnel control algorithm can realize the collaborative design of the path planning and control of the wheeled mobile robot, which effectively improve computing efficiency and simplify computational complexity. The study can expand the application range of signal temporal logic in mobile robot path planning, and increase the system robustness.

Modeling and predicting changes in the function and structure of the terrestrial biosphere and its feedbacks to climate change strongly depends on our ability to accurately represent interactions of the carbon and water cycles and energy exchange. However, carbon fluxes, hydrological status, and energy exchange simulated by process-based terrestrial ecosystem models are subject to significant uncertainties, largely due to the poorly calibrated parameters. In this work, an adjoint-based data assimilation system (Nanjing University Carbon Assimilation System; NUCAS v1.0) was developed, which is capable of assimilating multiple observations to optimize process parameters of a satellite-data-driven ecosystem model – the Biosphere–atmosphere Exchange Process Simulator (BEPS). Data assimilation experiments were conducted to investigate the robustness of NUCAS and to test the feasibility and applicability of assimilating carbonyl sulfide (COS) fluxes from seven sites to enhance our understanding of stomatal conductance and photosynthesis. Results showed that NUCAS is able to achieve a consistent fit to COS observations across various ecosystems, including evergreen needleleaf forest, deciduous broadleaf forest, C 3 grass, and C 3 crop. Comparing model simulations with validation datasets, we found that assimilating COS fluxes notably improves the model performance in gross primary productivity and evapotranspiration, with average root-mean-square error (RMSE) reductions of 23.54 % and 16.96 %, respectively. We also showed that NUCAS is capable of constraining parameters through assimilating observations from two sites simultaneously and achieving a good consistency with single-site assimilation. Our results demonstrate that COS can provide constraints on parameters relevant to water, energy, and carbon processes with the data assimilation system and opens new perspectives for better understanding of the ecosystem carbon, water, and energy exchanges. [ABSTRACT FROM AUTHOR]

Accurately modeling gross primary productivity (GPP) is of great importance for diagnosing terrestrial carbon–climate feedbacks. Process-based terrestrial ecosystem models are often subject to substantial uncertainties, primarily attributed to inadequately calibrated parameters. Recent research has identified carbonyl sulfide (COS) as a promising proxy of GPP due to the close linkage between leaf exchange of COS and carbon dioxide (CO2) through their shared pathway of stomatal diffusion. However, most of the current modeling approaches for COS and CO2 do not explicitly consider the vegetation structural impacts, i.e., the differences between the sunlit and shaded leaves in COS uptake. This study used ecosystem COS fluxes from seven sites to optimize GPP estimation across various ecosystems with the Biosphere-atmosphere Exchange Process Simulator (BEPS), which was further developed to simulate the canopy COS uptake under its state-of-the-art two-leaf framework. Our results demonstrated substantial improvement in GPP simulation across various ecosystems through the data assimilation of COS flux into the two-leaf model, with the ensemble mean of the root mean square error (RMSE) for simulated GPP reduced by 20.16 % to 64.12 %. Notably, we also shed light on the remarkable identifiability of key parameters within the BEPS model, including the maximum carboxylation rate of RuBisCO at 25 °C (Vcmax25), minimum stomatal conductance (bH2O), and leaf nitrogen content (Nleaf), despite intricate interactions among COS-related parameters. Furthermore, our global sensitivity analysis delineated both shared and disparate sensitivities of COS and GPP to model parameters and suggested the unique treatment of parameters for each site in COS and GPP modeling. In summary, our study deepened insights into the sensitivity, identifiability, and interactions of parameters related to COS and showcased the efficacy of COS in reducing uncertainty in GPP simulations. [ABSTRACT FROM AUTHOR]

Modeling and predicting changes in the function and structure of the terrestrial biosphere and its feedbacks to climate change strongly depends on our ability to accurately represent interactions of the carbon and water cycles, and energy exchange. However, carbon fluxes, hydrological status and energy exchange simulated by process-based terrestrial ecosystem models are subject to significant uncertainties, largely due to the poorly calibrated parameters related to various processes. In this work, an adjoint-based data assimilation system (Nanjing University Carbon Assimilation System, NUCAS) was developed, which is capable of assimilating multiple observations to optimize process parameters of a satellite data driven ecosystem model–BEPS (Boreal Ecosystem Productivity Simulator). Data assimilation experiments were conducted to demonstrate the robustness and to investigate the feasibility and applicability of NUCAS on seven sites by assimilating the carbonyl sulfide (COS) fluxes, which were tightly related to the stomatal conductance and photosynthesis. Results showed that NUCAS is able to achieve a consistent fit to COS observations across various ecosystems. Comparing prior simulations with validation datasets, we found that the assimilation of COS can significantly improve the model performance in gross primary productivity, sensible heat, latent heat and even soil moisture. We also showed that the NUCAS is capable of constraining parameters from multiple sites simultaneously and achieving a good consistency to the single-site assimilation. Our results demonstrate that COS can provide strong constraints on parameters relevant to water, energy and carbon processes with the data assimilation system, and open new perspectives for better understanding of the ecosystem carbon, water and energy exchanges. [ABSTRACT FROM AUTHOR]