Calibration on MEPDG Low Temperature Cracking Model and Recommendation on Asphalt Pavement Structures in Seasonal Frozen Region of China

In order to implement the Mechanistic-Empirical Pavement Design Guide (MEPDG) to design and maintain asphalt pavements in China, it is necessary to calibrate transfer functions of distresses in MEPDG with local conditions, including traffics, environment, and materials as well as measured pavement distresses data in field. Comprehensive single factor sensitivity analyses of factors that influence thermal cracking of asphalt pavements were conducted utilizing the MEPDG low temperature cracking (LTC) model. Additionally, multiple factor sensitivity analyses were carried out as well, based on which pavement structures with sound thermal cracking resistance were recommended for seasonal frozen regions in China. Finally, the field data of thermal cracks on typical asphalt pavements in China was utilized to calibrate the LTC model in MEPDG. An improvement was proposed on MEPDG LTC model, after which was applied, the predicted thermal cracking from MEPDG LTC model agrees well with measured thermal cracking in China

A Comparison of Hierarchical and Non-Hierarchical Bayesian Approaches for Fitting Allometric Larch (Larix.spp.) Biomass Equations

Accurate biomass estimations are important for assessing and monitoring forest carbon storage. Bayesian theory has been widely applied to tree biomass models. Recently, a hierarchical Bayesian approach has received increasing attention for improving biomass models. In this study, tree biomass data were obtained by sampling 310 trees from 209 permanent sample plots from larch plantations in six regions across China. Non-hierarchical and hierarchical Bayesian approaches were used to model allometric biomass equations. We found that the total, root, stem wood, stem bark, branch and foliage biomass model relationships were statistically significant (p-values \u3c 0.001) for both the non-hierarchical and hierarchical Bayesian approaches, but the hierarchical Bayesian approach increased the goodness-of-fit statistics over the non-hierarchical Bayesian approach. The R2 values of the hierarchical approach were higher than those of the non-hierarchical approach by 0.008, 0.018, 0.020, 0.003, 0.088 and 0.116 for the total tree, root, stem wood, stem bark, branch and foliage models, respectively. The hierarchical Bayesian approach significantly improved the accuracy of the biomass model (except for the stem bark) and can reflect regional differences by using random parameters to improve the regional scale model accuracy

A Wideband MIMO Channel Model for Aerial Intelligent Reflecting Surface-Assisted Wireless Communications

Compared to traditional intelligent reflecting surfaces(IRS), aerial IRS (AIRS) has unique advantages, such as more flexible deployment and wider service coverage. However, modeling AIRS in the channel presents new challenges due to their mobility. In this paper, a three-dimensional (3D) wideband channel model for AIRS and IRS joint-assisted multiple-input multiple-output (MIMO) communication system is proposed, where considering the rotational degrees of freedom in three directions and the motion angles of AIRS in space. Based on the proposed model, the channel impulse response (CIR), correlation function, and channel capacity are derived, and several feasible joint phase shifts schemes for AIRS and IRS units are proposed. Simulation results show that the proposed model can capture the channel characteristics accurately, and the proposed phase shifts methods can effectively improve the channel statistical characteristics and increase the system capacity. Additionally, we observe that in certain scenarios, the paths involving the IRS and the line-of-sight (LoS) paths exhibit similar characteristics. These findings provide valuable insights for the future development of intelligent communication systems.Comment: 6 pages, 7 figure

Recent Design Development in Molecular Imaging for Breast Cancer Detection Using Nanometer CMOS Based Sensors

As one of the key clinical imaging methods, the computed X-ray tomography can be further improved using new nanometer CMOS sensors. This will enhance the current technique's ability in terms of cancer detection size, position, and detection accuracy on the anatomical structures. The current paper reviewed designs of SOI-based CMOS sensors and their architectural design in mammography systems. Based on the existing experimental results, using the SOI technology can provide a low-noise (SNR around 87.8 db) and high-gain (30 v/v) CMOS imager. It is also expected that, together with the fast data acquisition designs, the new type of imagers may play important roles in the near-future high-dimensional images in additional to today's 2D imagers

Learning To Teach Large Language Models Logical Reasoning

Large language models (LLMs) have gained enormous attention from both academia and industry, due to their exceptional ability in language generation and extremely powerful generalization. However, current LLMs still output unreliable content in practical reasoning tasks due to their inherent issues (e.g., hallucination). To better disentangle this problem, in this paper, we conduct an in-depth investigation to systematically explore the capability of LLMs in logical reasoning. More in detail, we first investigate the deficiency of LLMs in logical reasoning on different tasks, including event relation extraction and deductive reasoning. Our study demonstrates that LLMs are not good reasoners in solving tasks with rigorous reasoning and will produce counterfactual answers, which require us to iteratively refine. Therefore, we comprehensively explore different strategies to endow LLMs with logical reasoning ability, and thus enable them to generate more logically consistent answers across different scenarios. Based on our approach, we also contribute a synthesized dataset (LLM-LR) involving multi-hop reasoning for evaluation and pre-training. Extensive quantitative and qualitative analyses on different tasks also validate the effectiveness and necessity of teaching LLMs with logic and provide insights for solving practical tasks with LLMs in future work

Circular polarized incident light scattering properties at optical clearing in tissues

This paper focuses on polarization imaging during optical clearing process in tissues due to refractive index matching of tissue structural components. We start with some single-dispersed tissue models, composed of large spheres, small spheres, and large cylinders, respectively. Along with the simulated refractive index matching inside and outside the scatterers, the linear polarized incident photons show similar decreased depolarization. It is worth noting that the circular polarized incident light show different polarization change for different scatterers, sensitive to scatterer size and shape. For small Rayleigh-like spherical scatterers, the circular depolarization also decreases with index matching. However, the depolarization by the larger scatterers can be enhanced, supported by the photon distribution change with the index matching in the backward detection. After some extreme points, the depolarization of circular polarized photons will be suppressed until almost disappear. Furthermore, by the simulation of hybrid-dispersed models, we can find out that the transmission of circular polarized photons during optical clearing, is more sensitive to the content of smaller scatterers in the turbid medium, and also has a close relationship with the proportion of the anisotropic scatterers. We also extract a character to describe the difference of linear and circular polarized photons. The value and the change of this character can help us to explain the main scatterers contributed to the polarization features of tissue-like medium during optical clearing. The above results indicate different polarization features for different scattering systems by optical clearing, which are potentially useful for studying optical clearing by polarization methods

Constructing tissue-like complex structures using cell-laden DNA hydrogel bricks

Tissue engineering has long been a challenge because of the difficulty of addressing the requirements that such an engineered tissue must meet. In this paper, we developed a new "brick-to-wall" based on unique properties of DNA supramolecular hydrogels to fabricate three-dimensional (3D) tissuelike structures: different cell types are encapsulated in DNA hydrogel bricks which are then combined to build 3D structures. Signal responsiveness of cells through the DNA gels was evaluated and it was discovered that the gel permits cell migration in 3D. The results demonstrated that this technology is convenient, effective and reliable for cell manipulation, and we believe that it will benefit artificial tissue fabrication and future large-scale production