Optimization of HPLC Detection of PMP Derivatives of Carbohydrates

Detection of carbohydrates has always been a big challenge in the world, which is still attracting numerous researchers to develop different methods to overcome various difficulties. Reducing sugars, a special group of carbohydrates containing a reducing end, have provided a possibility to combine one or more chromophores to facilitate the carbohydrate detection in spite of the lack of chromophoric group in original carbohydrates. After such kind of chemical derivatizations, the sugar derivatives can be analyzed by high performance liquid chromatography (HPLC) with ultraviolet detector (UV) and diode array detector (DAD), which have been the most common methods for the carbohydrate detection. In order to optimize the sugar detection via the HPLC-UV and/or DAD, this study applied the chemical derivatization to add an extra luminophore into carbohydrates molecules, for which 1-phenyl-3-methyl-5-pyrazolone (PMP) was used in this experiment. The optimal conditions for derivatizations of glucose and glucosamine with PMP were obtained through the response surface methodology (RSM) experimental design, which suggested the optimal conditions, under a fixed value at pH 13 of the buffer solution, for the glucose-PMP and glucosamine-PMP derivatizations at 71°C for 134 minutes and 73°C for 96 minutes, respectively. The delicate difference among the optimal conditions might result from the difference of the inner-structure and inner environmental pH values of the carbohydrates. Nevertheless, this method has been proven to be a feasible and practical method with high sensitivity to determine the most monosaccharides except fructose, and disaccharides such as lactose and maltose, as well as oligosaccharides which contain the reducing end. In addition to the effect of inner pH environment, multiple sugar rings and optical isomerism of carbohydrates might also play important roles in the yield of sugar-PMP derivatives. Furthermore, this research involved the study of the detective power in terms of the detective sensitivity, accuracy and linearity of two common detectors, i.e., DAD and evaporative light scattering detector (ELSD), on the sugar-PMP derivatives, and the efficiency in terms of the separation capability of two common HPLC columns, i.e., C18 column and amide column. Because of different principles of DAD and ELSD in chemical detection, both popular detectors have different sensitivities and selectivities for carbohydrates. DAD is able to analyze the sugar-PMP derivatives, while ELSD is good at detecting both the PMP free sugars, sugar PMP derivatives and other sugar derivatives such as sugar alcohols, etc. Moreover, the results have demonstrated that the amide column could efficiently separate the PMP free carbohydrates rather than the sugar-PMP derivatives, and on the contrary, the C18 column was able to separate the sugar-PMP derivatives rather than the sugar themselves

Feedback-efficient Active Preference Learning for Socially Aware Robot Navigation

Socially aware robot navigation, where a robot is required to optimize its trajectory to maintain comfortable and compliant spatial interactions with humans in addition to reaching its goal without collisions, is a fundamental yet challenging task in the context of human-robot interaction. While existing learning-based methods have achieved better performance than the preceding model-based ones, they still have drawbacks: reinforcement learning depends on the handcrafted reward that is unlikely to effectively quantify broad social compliance, and can lead to reward exploitation problems; meanwhile, inverse reinforcement learning suffers from the need for expensive human demonstrations. In this paper, we propose a feedback-efficient active preference learning approach, FAPL, that distills human comfort and expectation into a reward model to guide the robot agent to explore latent aspects of social compliance. We further introduce hybrid experience learning to improve the efficiency of human feedback and samples, and evaluate benefits of robot behaviors learned from FAPL through extensive simulation experiments and a user study (N=10) employing a physical robot to navigate with human subjects in real-world scenarios. Source code and experiment videos for this work are available at:https://sites.google.com/view/san-fapl.Comment: To appear in IROS 202

the Impact of Corporate Governance Mechanism on Firm Performance: Empirical Evidence in China

The purpose of this study is to test the impact of corporate governance determinants on firm performance. In this research, panel data that consists of 98 China listed companies under SSE 180 Index during a 5-year period from 2014 to 2018 is utilized. In the sample of selected companies, statistical results show a statically significant and economically important relationship between governance determinants and firm performance which is measured by Tobin’s Q and return on assets respectively. The governance determinants that I examine in this study include board size, board independence, CEO duality, ownership concentration, audit committee independence and executive remuneration. The findings reveal that selected determinants are associated with performance significantly

Husformer: A Multi-Modal Transformer for Multi-Modal Human State Recognition

Human state recognition is a critical topic with pervasive and important applications in human-machine systems.Multi-modal fusion, the combination of metrics from multiple data sources, has been shown as a sound method for improving the recognition performance. However, while promising results have been reported by recent multi-modal-based models, they generally fail to leverage the sophisticated fusion strategies that would model sufficient cross-modal interactions when producing the fusion representation; instead, current methods rely on lengthy and inconsistent data preprocessing and feature crafting. To address this limitation, we propose an end-to-end multi-modal transformer framework for multi-modal human state recognition called Husformer.Specifically, we propose to use cross-modal transformers, which inspire one modality to reinforce itself through directly attending to latent relevance revealed in other modalities, to fuse different modalities while ensuring sufficient awareness of the cross-modal interactions introduced. Subsequently, we utilize a self-attention transformer to further prioritize contextual information in the fusion representation. Using two such attention mechanisms enables effective and adaptive adjustments to noise and interruptions in multi-modal signals during the fusion process and in relation to high-level features. Extensive experiments on two human emotion corpora (DEAP and WESAD) and two cognitive workload datasets (MOCAS and CogLoad) demonstrate that in the recognition of human state, our Husformer outperforms both state-of-the-art multi-modal baselines and the use of a single modality by a large margin, especially when dealing with raw multi-modal signals. We also conducted an ablation study to show the benefits of each component in Husformer

Reliability Analysis of Correlated Competitive and Dependent Components Considering Random Isolation Times

Funding Information: Funding Statement: This work was supported by the National Natural Science Foundation of China (NSFC) (Grant No. 62172058) and the Hunan Provincial Natural Science Foundation of China (Grant Nos. 2022JJ10052, 2022JJ30624). Publisher Copyright: © 2023 Tech Science Press. All rights reserved.Peer reviewedPublisher PD

An Intelligent Secure Adversarial Examples Detection Scheme in Heterogeneous Complex Environments

Funding Information: Funding Statement: This work was supported in part by the Natural Science Foundation of Hunan Province under Grant Nos. 2023JJ30316 and 2022JJ2029, in part by a project supported by Scientific Research Fund of Hunan Provincial Education Department under Grant No. 22A0686, and in part by the National Natural Science Foundation of China under Grant No. 62172058. This work was also funded by the Researchers Supporting Project (No. RSP2023R102) King Saud University, Riyadh, Saudi Arabia.Peer reviewedPublisher PD

Model reduction of LFT systems

The notion of balanced realizations and balanced truncation model reduction, including guaranteed error bounds, is extended to general Q-stable linear fractional transformations (LFTs). Since both multidimensional and uncertain systems are naturally represented using LFTs, this can be interpreted either as doing state order reduction for multidimensional systems or as uncertainty simplification in the case of uncertain systems. The role of Lyapunov equations in the 1D theory is replaced by linear matrix inequalities (LMIs). All proofs are given in detail as they are very short and greatly simplify even the standard 1D case

Effects of Pressure and Doping on Ruddlesden-Popper phases Lan+1NinO3n+1

Recently the discovery of superconductivity with a critical temperature Tc up to 80 K in Ruddlesden-Popper phases Lan+1NinO3n+1 (n = 2) under pressure has garnered considerable attention. Up to now, the superconductivity was only observed in La3Ni2O7 single crystal grown with the optical-image floating zone furnace under oxygen pressure. It remains to be understood the effect of chemical doping on superconducting La3Ni2O7 as well as other Ruddlesden-Popper phases. Here, we systematically investigate the effect of external pressure and chemical doping on polycrystalline Ruddlesden-Popper phases. Our results demonstrate the application of pressure and doping effectively tunes the transport properties of Ruddlesden-Popper phases. We find pressure-induced superconductivity up to 86 K in La3Ni2O7 polycrystalline sample, while no signatures of superconductivity are observed in La2NiO4 and La4Ni3O10 systems under high pressure up to 50 GPa. Our study sheds light on the exploration of high-Tc superconductivity in nickelates.Comment: 21 papes, 8 figures and 1 tabl

Effect of physical and chemical pressure on the superconductivity of caged-type quasiskutterudite Lu5Rh6Sn18

Lu5Rh6Sn18 is one of the caged-type quasiskutterudite superconductors with superconducting transition temperature Tc = 4.12 K. Here, we investigate the effect of pressure on the superconductivity in Lu5Rh6Sn18 by combining high pressure electrical transport, synchrotron x-ray diffraction (XRD) and chemical doping. Application of high pressure can enhance both the metallicity and the superconducting transition temperature in Lu5Rh6Sn18. Tc is found to show a continuous increase reaching up to 5.50 K at 11.4 GPa. Our high pressure synchrotron XRD measurements demonstrate the stability of the pristine crystal structure up to 12.0 GPa. In contrast, Tc is suppressed after the substitution of La ions in Lu sites, inducing negative chemical pressure. Our study provides valuable insights into the improvement of superconductivity in caged compounds.Comment: 9 pages, 8 figure

Blockchain for the metaverse: A Review

Since Facebook officially changed its name to Meta in Oct. 2021, the metaverse has become a new norm of social networks and three-dimensional (3D) virtual worlds. The metaverse aims to bring 3D immersive and personalized experiences to users by leveraging many pertinent technologies. Despite great attention and benefits, a natural question in the metaverse is how to secure its users’ digital content and data. In this regard, blockchain is a promising solution owing to its distinct features of decentralization, immutability, and transparency. To better understand the role of blockchain in the metaverse, we aim to provide an extensive survey on the applications of blockchain for the metaverse. We first present a preliminary to blockchain and the metaverse and highlight the motivations behind the use of blockchain for the metaverse. Next, we extensively discuss blockchain-based methods for the metaverse from technical perspectives, such as data acquisition, data storage, data sharing, data interoperability, and data privacy preservation. For each perspective, we first discuss the technical challenges of the metaverse and then highlight how blockchain can help. Moreover, we investigate the impact of blockchain on key-enabling technologies in the metaverse, including Internet-of-Things, digital twins, multi-sensory and immersive applications, artificial intelligence, and big data. We also present some major projects to showcase the role of blockchain in metaverse applications and services. Finally, we present some promising directions to drive further research innovations and developments toward the use of blockchain in the metaverse in the future