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With the acceleration of innovative drugs launched in the market, clinical trials have become increasingly complex and costly. Integration of patients' perspective and engagement into the clinical research process is essential to the success of the trials, which leads to the emergence of the concept of patient-centric clinical trials. Compared to the conventional paradigm of clinical trials, patient-centric clinical trials have the potential to increase the feasibility of study protocol, to speed recruitment and execution, and to promote the success of trials. It calls for the institutions and investigators of clinical trials to be aware of and adapt to patient-centric initiatives. In this article, we highlight technologies and actions that can be used to enhance patients' engagement in the entire process of drug development, covering protocol development, trial conduction, and result publication.

When excavating the rock foundation of a hydropower station, it will be affected by the phenomenon of unloading and relaxation, which may increase the risk of stability of the dam foundation engineering system. The dam foundation of Baihetan Hydropower is a columnar jointed rock mass (CJRM), which presents strong brittleness and anisotropy compared to traditional dam foundation rocks. Therefore, this type of rock mass is prone to disturbance to the dam body, structure, etc. during excavation, so it is necessary to accurately evaluate the impact of dam foundation excavation. Establishing a rock mass creep models serve as an effective tool for evaluating such stability but often suffer from significant parameter uncertainty. Digital twin technology, a virtual model, is capable of real-time learning from actual monitoring data obtained from the physical entity to enhance the performance of the built-in mechanistic model. In this study, the researchers employ the classical Burgers constitutive equation as the theoretical framework and integrate it with an ensemble smoother with multiple data assimilation (ESMDA) method based on Bayesian principles, along with displacement monitoring data from the Baihetan Dam foundation, to construct a digital twin model. Within this framework, the researchers analyze the uncertainty of rheological parameters at various measurement points in the Baihetan Dam foundation. Subsequently, the most suitable rheological parameters are selected and incorporated into the constitutive model to obtain displacement estimates, which are then compared with on-site monitoring data. The results demonstrate that the proposed method effectively performs probabilistic parameter estimation and model prediction for rheological mechanics. This research integrates data-driven methods with mechanical principles, offering a reliable approach for assessing the uncertainty of unloading rheological parameters and displacement prediction in dam foundations, thereby providing essential support for the evaluation of excavation projects in the CJRM of the Baihetan Dam foundation. [ABSTRACT FROM AUTHOR]

In constructing hydraulic tunnels, construction disturbances and complex geological conditions can induce variations in the surrounding rock parameters. To navigate the complex non-linear interplay between rock material parameters and tunnel displacement during construction, this study proposes a hybrid learning model. It employs particle swarm optimization (PSO) to refine the hyperparameters of the eXtreme Gradient Boosting (XGBoost) technique. Sensitivity analysis and inversion of rock parameters is performed by using orthogonal design and the Sobol method to analyze the sensitivity of environmental and rock material factors. The findings indicate that the tunnel depth, elastic modulus, and Poisson ratio are particularly sensitive parameters. Mechanical parameters of the rock mass, identified through sensitivity analysis, are the focal point of this research and are integrated into a three-dimensional computational model. The resulting tunnel displacement calculations serve as datasets for the inversion of the actual engineering project's surrounding rock mechanical parameters. These inverted parameters were fed into the FLAC3D software (version 7.0), yielding results that align closely with field measurements, which affirms the PSO-XGBoost model's validity and precision. The insights garnered from this research offer a substantial reference for determining rock mass parameters in tunnel engineering amidst complex conditions. [ABSTRACT FROM AUTHOR]