STUDY ON THE PERFORMANCE OF HIGH-MODULUS ASPHALT CONCRETE PAVEMENT IN EXTREME CURVES OR STEEP SLOPES OF TRUNK HIGHWAY

With the purpose of the project, we determined the performance of high modulus asphalt concrete (HMAC) pavement in sharp curves or steep slopes of the trunk highway. We selected bending of road surface, bending and stretching strain at the bottom of surface layer, vertical compressive strain at the bottom of surface layer as research parameter index. By using the three-dimensional model analysis function of the finite element software ANSYS, the mechanical models of asphalt pavement with three different structures under the action of steep slope and heavy traffic are established. Firstly, the conventional asphalt pavement consists of 4cmAC-13 bituminous pavement (the top layer) and 6cmAC-20 bituminous pavement (the bottom layer). Then, the HMAC pavement 1 consists of 4cmAC-13 bituminous pavement (the top layer) and 6cmAC-EME14 bituminous pavement (the bottom layer).The HMAC pavement 2 consists of 6cmAC-EME14 bituminous pavement (the top layer) and 4cmAC-13 bituminous pavement (the bottom layer). Then we tried it out that for the deflection value, the HMAC pavement 1 was 5.34 percentage point reduced than the conventional asphalt pavement. At the same time, the HMAC pavement 2 was 6.95 percentage point reduced than the conventional asphalt pavement. So, it can significantly reduce the bending strain at the bottom of the surface layer by using HMAC as asphalt pavement structure. For the resistance to shear strain and vertical compressive strain at the bottom of the surface layer, the HMAC pavement 1 is the best. Then the HMAC pavement 2 follows and then the conventional asphalt pavement. The results show that the HMAC can significantly improve the overall stiffness of the pavement and reduce the bending, shearing and vertical strain. Meanwhile, it can also reduce the occurrence of wheel rut, upheaval, fatigue crack and other common diseases

The dotTHz Project: A Standard Data Format for Terahertz Time-Domain Data and Elementary Data Processing Tools

From investigating molecular vibrations to observing galaxies, terahertz technology has found extensive applications in research and development over the past three decades. Terahertz time-domain spectroscopy and imaging have experienced significant growth and now dominate spectral observations ranging from 0.1 to 10 THz. However, the lack of standardised protocols for data processing, dissemination, and archiving poses challenges in collaborating and sharing terahertz data between research groups. To tackle these challenges, we present the dotTHz project, which introduces a standardised terahertz data format and the associated open-source tools for processing and interpretation of dotTHz files. The dotTHz project aims to facilitate seamless data processing and analysis by providing a common framework. All software components are released under the MIT licence through GitHub repositories to encourage widespread adoption, modification, and collaboration. We invite the terahertz community to actively contribute to the dotTHz project, fostering the development of additional tools that encompass a greater breadth and depth of functionality. By working together, we can establish a comprehensive suite of resources that benefit the entire terahertz community

Complex Locomotion Skill Learning via Differentiable Physics

Differentiable physics enables efficient gradient-based optimizations of neural network (NN) controllers. However, existing work typically only delivers NN controllers with limited capability and generalizability. We present a practical learning framework that outputs unified NN controllers capable of tasks with significantly improved complexity and diversity. To systematically improve training robustness and efficiency, we investigated a suite of improvements over the baseline approach, including periodic activation functions, and tailored loss functions. In addition, we find our adoption of batching and an Adam optimizer effective in training complex locomotion tasks. We evaluate our framework on differentiable mass-spring and material point method (MPM) simulations, with challenging locomotion tasks and multiple robot designs. Experiments show that our learning framework, based on differentiable physics, delivers better results than reinforcement learning and converges much faster. We demonstrate that users can interactively control soft robot locomotion and switch among multiple goals with specified velocity, height, and direction instructions using a unified NN controller trained in our system. Code is available at https://github.com/erizmr/Complex-locomotion-skill-learning-via-differentiable-physics

Cartilage compositional MRI-a narrative review of technical development and clinical applications over the past three decades.

Articular cartilage damage and degeneration are among hallmark manifestations of joint injuries and arthritis, classically osteoarthritis. Cartilage compositional MRI (Cart-C MRI), a quantitative technique, which aims to detect early-stage cartilage matrix changes that precede macroscopic alterations, began development in the 1990s. However, despite the significant advancements over the past three decades, Cart-C MRI remains predominantly a research tool, hindered by various technical and clinical hurdles. This paper will review the technical evolution of Cart-C MRI, delve into its clinical applications, and conclude by identifying the existing gaps and challenges that need to be addressed to enable even broader clinical application of Cart-C MRI

Impact of immediate release film coating on the disintegration process of tablets

Pharmaceutical tablets are often coated with a layer of polymeric material to protect the drug from environmental degradation, facilitate the packaging process, and enhance patient compliance. However, the detailed effects of such coating layers on drug release are not well understood. To investigate this, flat-faced pure microcrystalline cellulose tablets with a diameter of 13 mm and a thickness between 1.5 mm to 1.6 mm were directly compressed, and a film coating layer with a thickness of 80 μm to 120 μm was applied to one face of these tablets. This tablet geometry and immediate release film coating were chosen as a model system to understand how the film coating interacts with the tablet core. The coating hydration and dissolution process was studied using terahertz pulsed imaging, while optical coherence tomography was used to capture further details on the swelling process of the polymer in the coated tablet. The study investigated the film coating polymer dissolution process and found the gelling of dissolving polymer restricted the capillary liquid transport in the core. These findings can help predict the dissolution of film coating within the typical range of thickness (30 μm to 40 μm) and potentially be extended to understand modified release coating formulations

Convolutional Neural Networks for Classification of T2DM Cognitive Impairment Based on Whole Brain Structural Features

PurposeCognitive impairment is generally found in individuals with type 2 diabetes mellitus (T2DM). Although they may not have visible symptoms of cognitive impairment in the early stages of the disorder, they are considered to be at high risk. Therefore, the classification of these patients is important for preventing the progression of cognitive impairment.MethodsIn this study, a convolutional neural network was used to construct a model for classifying 107 T2DM patients with and without cognitive impairment based on T1-weighted structural MRI. The Montreal cognitive assessment score served as an index of the cognitive status of the patients.ResultsThe classifier could identify T2DM-related cognitive decline with a classification accuracy of 84.85% and achieved an area under the curve of 92.65%.ConclusionsThe model can help clinicians analyze and predict cognitive impairment in patients and enable early treatment