Flexural Behavior of RC Beams Under Combined Effects of Acid–Salt Mist and Carbon Dioxide

The coupling effects of hydrochloric acid mist, carbon dioxide, and salt mist rich in Cl– and SO42– on the degradation of reinforcement concrete (RC) beams were researched with the simulation of colliery ground environment (CGE) and experimental investigation. The results indicated that carbonation of concrete and corrosion of rebar increased slowly as the maximum width of crack became \u3c0.5 mm. Meanwhile, the flexural carrying capacity of the deteriorated beam decreased slightly, while the concrete strength got a small increase first and a large decrease of more than 20% quickly. As the width of crack exceeded 0.5 mm, each target changed rapidly except the carbonation depth. Because of the interaction of deteriorated concrete and corroded rebar, the crack width, and flexural behavior of the beams have discrete correlation with the corrosion of rebar. The failure mode of beams changed from the crushing of compression concrete to the yielding of rebar

NeuralMPS: Non-Lambertian Multispectral Photometric Stereo via Spectral Reflectance Decomposition

Multispectral photometric stereo(MPS) aims at recovering the surface normal of a scene from a single-shot multispectral image captured under multispectral illuminations. Existing MPS methods adopt the Lambertian reflectance model to make the problem tractable, but it greatly limits their application to real-world surfaces. In this paper, we propose a deep neural network named NeuralMPS to solve the MPS problem under general non-Lambertian spectral reflectances. Specifically, we present a spectral reflectance decomposition(SRD) model to disentangle the spectral reflectance into geometric components and spectral components. With this decomposition, we show that the MPS problem for surfaces with a uniform material is equivalent to the conventional photometric stereo(CPS) with unknown light intensities. In this way, NeuralMPS reduces the difficulty of the non-Lambertian MPS problem by leveraging the well-studied non-Lambertian CPS methods. Experiments on both synthetic and real-world scenes demonstrate the effectiveness of our method

Learning to Decompose Visual Features with Latent Textual Prompts

Recent advances in pre-training vision-language models like CLIP have shown great potential in learning transferable visual representations. Nonetheless, for downstream inference, CLIP-like models suffer from either 1) degraded accuracy and robustness in the case of inaccurate text descriptions during retrieval-based inference (the challenge for zero-shot protocol); or 2) breaking the well-established vision-language alignment (the challenge for linear probing). To address them, we propose Decomposed Feature Prompting (DeFo). DeFo leverages a flexible number of learnable embeddings as textual input while maintaining the vision-language dual-model architecture, which enables the model to learn decomposed visual features with the help of feature-level textual prompts. We further use an additional linear layer to perform classification, allowing a scalable size of language inputs. Our empirical study shows DeFo's significance in improving the vision-language models. For example, DeFo obtains 73.2% test accuracy on ImageNet with a ResNet-50 backbone without tuning any pretrained weights of both the vision and language encoder, outperforming zero-shot CLIP by a large margin of 15.0%, and outperforming state-of-the-art vision-language prompt tuning method by 7.6%

Fluid‐driven soft CoboSkin for safer human–robot collaboration: fabrication and adaptation

In human–robot collaboration, the wrapping material on robots is not only required to have the sensing ability to adapt to the external environment but also need to have the function of cushioning the collision between human and robot. Herein, a fluid‐driven soft robot skin with sensing and actuating function is successfully applied to a collaborative robot and working well with the host robot. The skin is an integration of sponge force sensors and pneumatic actuators. By altering the internal air pressure in pneumatic actuators, the developed robot skin can provide more than ten times tunable stiffness and sensitivity. In addition, the skin can reduce the peak force of the collision and achieve the actuating function. Using three‐dimensional printing and computer‐aided design, the skin is fabricated and attached to a collaborative robot conformally. Drawing upon the data acquisition and control system, the experiment for illustrating the applications of the CoboSkin is successfully performed. The skin provides the robot with multi‐functions, which are similar to the human muscle and skin attached to human bones. By mimicking human skin and muscle with tactile sensing function and stiffness tuning function, CoboSkin can enhance the adaptability of the robot to human daily life

A high-performance quantum dot superluminescent diode with a two-section structure

Based on InAs/GaAs quantum dots [QDs], a high-power and broadband superluminescent diode [SLD] is achieved by monolithically integrating a conventional SLD with a semiconductor optical amplifier. The two-section QD-SLD device exhibits a high output power above 500 mW with a broad emission spectrum of 86 nm. By properly controlling the current injection in the two sections of the QD-SLD device, the output power of the SLD can be tuned over a wide range from 200 to 500 mW while preserving a broad emission spectrum based on the balance between the ground state emission and the first excited state emission of QDs. The gain process of the two-section QD-SLD with different pumping levels in the two sections is investigated

Study of ‘Fingerprints’ for Green Tea from Different Planting Areas in Eastern China

Green tea is one of the main teas in China, which is unfermented and retains more natural substances of fresh tea leaves. This is the preliminary study of  application of ‘fingerprints’ based on differences in component composition of green tea. Five green teas from different areas in eastern China are  analyzed, which are processed by microwave-assisted solvent (ethanol) extraction method to obtain tea polyphenols, flavonoids, polysaccharides,  pigments (thearubigins, theaflavins, theabrownins). The results show that the component composition of five green teas are varied from each other;  based on these contents varieties, we have constructed a ‘fingerprint’ and applied linear discriminant analysis (LDA) and hierarchical cluster analysis  (HCA) to assist in the identification of these five green teas. This method does not require large, expensive instruments (such as high performance liquid  chromatograph, gas chromatograph, mass spectrometer, etc.), and is easy to use, which provides a new avenue for the identification of tea.&nbsp