Policy agenda for sustainable intermodal transport in China: An application of the multiple streams framework

Intermodal transport is widely believed to be an efficient way of organizing transportation activities because of its significant role in reducing logistics costs and emissions of air pollutants, which copes with the ever-increasing economic and environmental concerns. This paper applies the multiple streams framework (MSF) to analyze three streams (e.g., the problem stream, policy stream, and politics stream) in setting policy agenda for sustainable intermodal transport in China. By restricting the attention to the opening of the policy window and the coupling of the three streams, the motivation, process, and trend of formulating intermodal transport policy are systematically discussed. The findings show that the key to setting the policy agenda for sustainable intermodal transport in China is to strengthen collaboration among multiple interest groups, boost the national mood, and diversify the identity of policy entrepreneurs. This paper not only verifies the applicability of the MSF, but also helps us to better understand how sustainable intermodal transport policy is formulated in China, thus promoting future policy making

1-[4-(2-Chloro­eth­oxy)-2-hy­droxy­phen­yl]ethanone

In the title compound, C10H11ClO3, obtained by the reaction of 2,4-dihy­droxy­acetophenone, potassium carbonate and 1-bromo-2-chloro­ethane, an intra­molecular O—H⋯O hydrogen bond occurs

Ethyl 1-(4-chloro­benz­yl)-3-(4-fluoro­phen­yl)-1H-pyrazole-5-carboxyl­ate

In the title compound, C19H16ClFN2O2, the pyrazole ring makes dihedral angles of 5.15 (6) and 77.72 (6)°, with the fluoro­phenyl and chloro­phenyl rings, respectively

A reinforcement learning framework for dynamic mediation analysis

Mediation analysis learns the causal effect transmitted via mediator variables between treatments and outcomes, and receives increasing attention in various scientific domains to elucidate causal relations. Most existing works focus on pointexposure studies where each subject only receives one treatment at a single time point. However, there are a number of applications (e.g., mobile health) where the treatments are sequentially assigned over time and the dynamic mediation effects are of primary interest. Proposing a reinforcement learning (RL) framework, we are the first to evaluate dynamic mediation effects in settings with infinite horizons. We decompose the average treatment effect into an immediate direct effect, an immediate mediation effect, a delayed direct effect, and a delayed mediation effect. Upon the identification of each effect component, we further develop robust and semi-parametrically efficient estimators under the RL framework to infer these causal effects. The superior performance of the proposed method is demonstrated through extensive numerical studies, theoretical results, and an analysis of a mobile health dataset. A Python implementation of the proposed procedure is available at https://github.com/linlinlin97/MediationRL

Enabling Competitive Performance of Medical Imaging with Diffusion Model-generated Images without Privacy Leakage

Deep learning methods have impacted almost every research field, demonstrating notable successes in medical imaging tasks such as denoising and super-resolution. However, the prerequisite for deep learning is data at scale, but data sharing is expensive yet at risk of privacy leakage. As cutting-edge AI generative models, diffusion models have now become dominant because of their rigorous foundation and unprecedented outcomes. Here we propose a latent diffusion approach for data synthesis without compromising patient privacy. In our exemplary case studies, we develop a latent diffusion model to generate medical CT, MRI and PET images using publicly available datasets. We demonstrate that state-of-the-art deep learning-based denoising/super-resolution networks can be trained on our synthetic data to achieve image quality equivalent to what the same network can achieve after being trained on the original data (the p values well exceeding the threshold of 0.05). In our advanced diffusion model, we specifically embed a safeguard mechanism to protect patient privacy effectively and efficiently. Consequently, every synthetic image is guaranteed to be different by a pre-specified threshold from the closest counterpart in the original patient dataset. Our approach allows privacy-proof public sharing of diverse big datasets for development of deep models, potentially enabling federated learning at the level of input data instead of local network weights.Comment: 30 pages, 3 figure

A Reinforcement Learning Framework for Dynamic Mediation Analysis

Mediation analysis learns the causal effect transmitted via mediator variables between treatments and outcomes and receives increasing attention in various scientific domains to elucidate causal relations. Most existing works focus on point-exposure studies where each subject only receives one treatment at a single time point. However, there are a number of applications (e.g., mobile health) where the treatments are sequentially assigned over time and the dynamic mediation effects are of primary interest. Proposing a reinforcement learning (RL) framework, we are the first to evaluate dynamic mediation effects in settings with infinite horizons. We decompose the average treatment effect into an immediate direct effect, an immediate mediation effect, a delayed direct effect, and a delayed mediation effect. Upon the identification of each effect component, we further develop robust and semi-parametrically efficient estimators under the RL framework to infer these causal effects. The superior performance of the proposed method is demonstrated through extensive numerical studies, theoretical results, and an analysis of a mobile health dataset

Microtension Test Method for Measuring Tensile Properties of Individual Cellulosic Fibers

A microtension testing system was devised to measure mechanical properties of individual cellulosic fibers. To avoid specimen gripping and to enhance fiber alignment during testing, a self-aligning ball and socket gripping assembly was used in the microtensile tester design. A resolution of 0.098 mN was obtained for the tensile load measurement with this microtensile tester. Fiber strain was determined from high-precision stepper motor movement with 0.078-μm resolution or by in situ video photography. Cross-sectional areas of a single fiber cell wall were measured with a confocal laser scanning microscope. Results obtained from this system indicated a linear stress-strain curve until fatal failure for mature latewood fibers, whereas juvenile latewood fibers displayed curvilinear stress-strain relationships. Average values of tensile strength, tensile modulus, and elongation at break were 1258 MPa, 19.9 GPa, and 6.6% for mature latewood fiber and 558 MPa, 8.5 GPa, and 9.9% for juvenile latewood fiber, respectively. These values agreed with published data. The preliminary test indicated the usefulness of the integrated environmental chamber for investigating moisture effect on fiber engineering properties, but further investigation is needed to obtain statistically significant data