Early age behavior of jointed plain concrete pavements subjected to environmental loads

Studies on deformation characteristics of early-age Jointed Plan Concrete Pavements (JPCP) subjected to pure environmental loading has drawn significant interest as it is believed that the early-age deformation of Portland Cement Concrete (PCC) slab could result in the loss of pavement smoothness and the tensile stresses induced by these deformations could result in early-age cracking. However, the complex interaction of several environmental factors has resulted in difficulties in predicting the JPCP deformation characteristics under environmental loading. Also, the effect of the resulting slab distortion on the initial JPCP smoothness has not been adequately addressed by previous research studies;In this study, two newly constructed JPCP test sections; one on highway US-34 near Burlington and the other on US-30 near Marshalltown, Iowa were instrumented and monitored during the critical time (seven days) immediately following construction during the summer of 2005. Temperature data and moisture data obtained from both sites were analyzed. The slab deformations associated with temperature and moisture were analyzed using measured vertical displacements and pavement surface profile measurements. Using the longitudinal surface profile measurements from different locations of the test section during the morning and afternoon diurnal cycles, the smoothness indices such as International Roughness Index (IRI) and Ride Number (RN) were computed;The early-age deformation behavior of instrumented JPCP under environmental loading was simulated using ISLAB 2000 (2.5-D) and EverFE 2.24 (3-D) Finite Element (FE) programs using the equivalent temperature difference concept. The changes in smoothness indices at different measurement times were investigated and compared with those obtained using FE simulations;This study shows that the early-age deformation behavior of PCC is influenced not only by temperature variation but also by other environmental factors such as the moisture variation, drying shrinkage and temperature condition during pavement construction. Even though it is observed that measurable changes of early-age pavement smoothness do occur between morning and afternoon, these variations are not statistically significant

Improved Abundance Sensitivity of Molecular Ions in Positive-Ion APCI MS Analysis of Petroleum in Toluene

Positive-ion atmospheric pressure chemical ionization (APCI) Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) analyses of petroleum sample were performed with higher sensitivity by switching the solvent composition from toluene and methanol or acetonitrile to a one-component system consisting only of toluene. In solvent blends, molecular ions were more abundant than were protonated ions with increasing percentages of toluene. In 100% toluene, the double-bond equivalence (DBE) distributions of molecular ions obtained by APCI MS for each compound class were very similar to those obtained in dopant assisted atmospheric pressure photo ionization (APPI) MS analyses. Therefore, it was concluded that charge-transfer reaction, which is important in toluene-doped APPI processes, also plays a major role in positive-ion APCI. In the DBE distributions of S1, S2, and SO heteroatom classes, a larger enhancement in the relative abundance of molecular ions at fairly specific DBE values was observed as the solvent was progressively switched to toluene. This enhanced abundance of molecular ions was likely dependent on molecular structure

Texture Learning Domain Randomization for Domain Generalized Segmentation

Deep Neural Networks (DNNs)-based semantic segmentation models trained on a source domain often struggle to generalize to unseen target domains, i.e., a domain gap problem. Texture often contributes to the domain gap, making DNNs vulnerable to domain shift because they are prone to be texture-biased. Existing Domain Generalized Semantic Segmentation (DGSS) methods have alleviated the domain gap problem by guiding models to prioritize shape over texture. On the other hand, shape and texture are two prominent and complementary cues in semantic segmentation. This paper argues that leveraging texture is crucial for improving performance in DGSS. Specifically, we propose a novel framework, coined Texture Learning Domain Randomization (TLDR). TLDR includes two novel losses to effectively enhance texture learning in DGSS: (1) a texture regularization loss to prevent overfitting to source domain textures by using texture features from an ImageNet pre-trained model and (2) a texture generalization loss that utilizes random style images to learn diverse texture representations in a self-supervised manner. Extensive experimental results demonstrate the superiority of the proposed TLDR; e.g., TLDR achieves 46.5 mIoU on GTA-to-Cityscapes using ResNet-50, which improves the prior state-of-the-art method by 1.9 mIoU. The source code is available at https://github.com/ssssshwan/TLDR.Comment: ICCV 202

Dynamics of Morphology-Dependent Resonances by Openness in Dielectric Disk for TE polarization

We have studied the dynamics of morphology-dependent resonances by openness in a dielectric microdisk for TE polarization. For the first time, we report that the dynamics exhibits avoided resonance crossings between inner and outer resonances even though the corresponding billiard is integrable. Due to the avoidance, inner and outer resonances can be exchanged and $Q$-factor of inner resonances is strongly affected. We analyze the diverse phenomena aroused from the dynamics including the avoided crossings.Comment: 6 pages, 5 figure