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Interference micro/nanolenses of salts for local modulation of Raman scattering
No full textMicro/nanolenses play a crucial role in optics and spectroscopy, but the effect of interference patterns within each lens has been largely unexplored. Herein, we investigate modulation of Raman scattering by the interference within a single micro/nanolens of a hygroscopic salt. Lenses having two different diameter (d) ranges, d > 2 mu m and d similar to 1 mu m, are placed on a silicon substrate, followed by collection of a Raman intensity map of the silicon peak. Lenses with d > 2 mu m show dark and bright circular fringes in the Raman map, resembling the Newton's rings formed by optical interference. In the smaller lenses (d similar to 1 mu m), the map yields only a single peak at the center, representing either an intensity maximum or minimum. In both diameter ranges, whether the Raman intensity is enhanced or suppressed is determined by interference conditions, such as wavelength of the excitation laser or thickness of the SiO2 layer. The interference in salt micro/nanolenses finds applications in local modulation of Raman scattering of a nanoscale object, as demonstrated in individual single-walled carbon nanotubes decorated with the salt lenses
Deep-learning model for predicting hardness and phase distributions from two cross-sectional temperature distribution images in laser heat treatment of AH36 steel
No full textLaser heat treatment of carbon steel is generally performed to increase the hardness of the specimen. However, when the heating temperature is high, softening of the specimen can occur along with melting. It is important to predict both hardening and softening processes, but such research has been limited so far. In this study, a deep learning model was developed that predicts both hardening and softening during laser heat treatment of AH36 steel using two cross-sectional temperature distributions obtained at 0.5-s intervals as inputs. Two temperature distributions were used as inputs to provide accurate information about cooling rate to the model. The input temperature distribution of the cross-section was calculated by solving the heat conduction equation using finite element method, and the hardness and phase distributions used as ground truth were obtained through a 2 kW multi-mode fiber laser experiment. The model was constructed based on a generative adversarial network with a residual network. It was found that when two temperature distributions were used, both softened and hardened areas were predicted accurately. However, when only one temperature distribution was used, only hardening could be predicted. On the other hand, both models could predict phase distributions, but much more accurate results were obtained when two temperature distributions were used as inputs. (c) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Estimation of 6D Pose of Objects Based on a Variant Adversarial Autoencoder
No full textThe goal of this paper is to estimate object???s 6D pose based on the texture-less dataset. The pose of each projection view is obtained by rendering the 3D model of each object, and then the orientation feature of the object is implicitly represented by the latent space obtained from the RGB image. The 3D rotation of the object is estimated by establishing the codebook based on a template matching architecture. To build the latent space from the RGB images, this paper proposes a network based on a variant Adversarial Autoencoder (Makhzani et al. in Computer Science, 2015). To train the network, we use the dataset without pose annotation, and the encoder and decoder do not have a structural symmetry. The encoder is inspired by the existing model (Yang et al. in proceedings of IJCAI, 2018), (Yang et al. in proceedings 11 of CVPR, 2019) that incorporates the function of feature extraction from two different streams. Based on this network, the latent feature vector that implicitly represents the orientation of the object is obtained from the RGB image. Experimental results show that the method in this paper can realize the 6D pose estimation of the object and the result accuracy is better than the advanced method (Sundermeyer et al. in proceedings of ECCV, 2018)
Causal inference based lifestyle coaching system for thyroid disease patients when lifestyle variables are continuous
No full textRecent progress and perspectives on heteroatom doping of hematite photoanodes for photoelectrochemical water splitting
No full textOver the past few decades, extensive research on photoelectrochemical (PEC) water splitting has been conducted as a promising solution to meet the increasing demand for cleaner and renewable energy in a sustainable manner. Among the various photocatalysts, hematite (alpha-Fe2O3) has gained significant attention due to its advantageous characteristics, such as a high theoretical solar-to-hydrogen conversion efficiency value, a suitable band gap energy for visible light absorption, chemical stability, and low cost. However, the high PEC potential of alpha-Fe2O3 is hindered by several limitations, including band gap mismatch, short hole diffusion length, and low electrical conductivity. Several modifications are necessary to enhance the viability of alpha-Fe2O3 as an efficient photocatalyst for PEC water splitting. This review article primarily focuses on strategies aimed at improving PEC water oxidation performance, especially by addressing its poor transport behavior through heteroatom doping. In particular, we explore the co-doping approach involving unintentional Sn dopants, which are diffused from the fluorine-doped tin oxide substrate during high-temperature annealing. Over the past few decades, extensive research on photoelectrochemical (PEC) water splitting has been conducted as a promising solution to meet the increasing demand for cleaner and renewable energy in a sustainable manner
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No full textSupply chain disruption response and recovery: The role of power and governance
No full textHow buyer-supplier relationships manage and recover from periods of distress is a critical managerial challenge. We examine this issue specifically by looking at buyer-supplier relationships that are recovering from a supply chain disruption. Governance studies have been central in the supplier management literature for some time, and we are motivated to understand the role of governance for relationships in distress and the impact of power dynamics in this context. We explore the impact of contractual and relational governance on disruption response and recovery and examine the moderating role of power that the buyer might leverage over their suppliers following a disruption. Power is conceptualised as threat of coercion and promise of reward, and thus reflects the two contrasting dimensions of power. Addressing common concerns with single respondent surveys, we used a cross-sectional survey to collect matched pair data from 239 US manufacturers and examined the dyadic perspectives of both buyers and suppliers, analysing the data using hierarchical OLS regression. We found that contractual and relational governance both enhance disruption response and recovery by encouraging supplier cooperation. We also report that threat of coercion from the buyer, and the promise of reward for a supplier, is not effective in reinforcing the positive impact that contractual governance has on disruption response and recovery performance. However, we find that the promise of a reward does enhance the effectiveness of relational governance in improving the response and recovery
