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Back-end optimization plays a key role in eliminating the accumulated error in Visual Simultaneous Localization And Mapping (VSLAM). Existing back-end optimization methods are usually premised on the Gaussian noise assumption which does not always hold true due to the non-convex nature of the image and the fact that non-Gaussian noises are often encountered in real scenes. In view of this, we propose a back-end optimization method based on Multi-Convex combined Maximum Correntropy Criterion (MCMCC). A MCMCC-based cost function is first tailored for nonlinear back-end optimization in the context of VSLAM and the optimization problem is solved through Levenberg–Marquardt algorithm iteratively. Then, the proposed method is applied to ORB-SLAM3 to test its performance on public indoor and outdoor datasets. The real time performance is also validated using a RaceBot platform in real indoor and outdoor environments. In addition, the reprojection error is statistically analyzed to demonstrate the non-Gaussian characteristics in the back-end optimization process. Finally, the suggestion parameters are also provided through experiments for further study. • The MCMCC-based cost function for VSLAM back-end optimization is designed and the optimization problem is then solved through Levenberg-Marquardt method iteratively. To our knowledge, this is the first time a ITL-based cost function is considered in nonlinear back-end optimization. • The proposed method is implemented in the visual mode in ORB-SLAM3 and a comprehensive analysis is performed by comparing the proposed method with other widely used robust kernel function-based methods in the context of VSLAM on EuRoC, TUM and KITTI datasets. In addition, the method is also tested in real environment. • The reprojection error in back-end is analyzed from the prospective of stochastic statistics to provide an insight on the back-end problem in non-Gaussian setting in VSLAM. • The suggestion parameters are provided as guidance for both indoor applications and outdoor applications. [ABSTRACT FROM AUTHOR]

With the era of big data, data-driven models are increasingly vital to just-in-time decision support in pollution emission management and planning. This article aims to evaluate the usability of the proposed data-driven model to monitor NOx emission from a coal-fired boiler process using easily measured process variables. As the emission process is highly complex, process variables interact with each other, and they cannot guarantee that all the variables in the actual operation obey the Gaussian distributions. As conventional principal component analysis (PCA) can only extract variance information, a novel data-driven model is proposed, called survival information potential-based PCA (SIP-PCA) model, is proposed in this work. First, an improved PCA model is established based on the SIP performance index. SIP-PCA can extract more information in the latent space from the process variables following the non-Gaussian distributions. Then, the control limits for fault detection are determined based on the kernel density estimation method. Finally, the proposed algorithm is successfully applied to a real NOx emission process. By monitoring the operation of process variables, possible failures can be detected as soon as possible. Fault isolation and system reconstruction can be implemented in time, preventing NOx emissions from exceeding its standard. [Display omitted] • Survival information potential based PCA (SIP-PCA) for fault detection is proposed. • The SIP-PCA method does not have any distribution assumptions and it is robust. • The SIP-PCA algorithm is successfully applied to the real NOx emission detection. • The timeliness and effectiveness of the NOx emission fault are detected. • SIP-PCA outperforms conventional algorithms. [ABSTRACT FROM AUTHOR]

The lack of tourism-specific tacit knowledge can prevent locals from fully participating in rural tourism development. Using a longitudinal case analysis, we explore how tacit knowledge transfer to locals can be facilitated and their capabilities can be enhanced. We find that experienced migrant workers from tourism firms can provide tacit knowledge specific to tourism operations. However, the absence of knowledge-related transaction activities and the lack of cross-group trust make the "migrant-local" knowledge transfer spontaneous, fragmented, and ineffective. We empirically demonstrate that an influential third party's intervention can promote tacit knowledge transfer by bearing transaction costs, initiating knowledge transfer plans, and coordinating relationships among participants. Finally, we summarize a generalized process by which different stakeholders jointly formalize tacit knowledge transfer practices, including identification, negotiation, implementation, and valuation. Overall, this study contributes to the theory and practice of knowledge management in rural tourism development. [ABSTRACT FROM AUTHOR]

We perform a numerical evaluation on improving the thermal transfer at the graphene/polypropylene (PP) interface with non-covalent functionalization techniques. The results indicate that it can significantly improve the interfacial thermal conductivity (ITC) of the graphene/polypropylene (PP) and this enhancement depends on the surface functionalization degree of graphene. We detected some fantastic phenomena that the chain length of straight alkane chains of functionalized molecules significantly affects the enhancement of ITC. The effect of the number of graphene layers was further studied. ITC decreases with the increase in the number of layers. Non-covalent functionalization still has a positive effect on multilayer graphene. The vibrational density of states (VDOS) analysis shows that non-covalent functionalization treatment would cause the phonon distribution of graphene to be broadened, and the phonon peak height would decrease as the layer number grows. It implies a detailed understanding of manipulating the ITC of graphene/polypropylene in practical thermal management applications. • We perform a numerical evaluation on improving the thermal transfer at the graphene/polypropylene (PP) interface. • We detected that the enhancement for ITC depends on the surface functionalization rate of graphene. • We detected some fantastic phenomena that graphene layer number on ITC. • It implies a detailed understanding for manipulating the ITR of graphene/polypropylene in thermal management. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we prove a blow-up criterion of strong solutions to the 3D viscous and non-resistive isentropic compressible magnetohydrodynamic equations with initial vacuum. This blow-up criterion depends only on the gradient of velocity, which is analogous to the one for the compressible Navier–Stokes equations (cf. Huang et al. (2010) ). [Copyright &y& Elsevier]

Learning rich representations efficiently plays an important role in the multi-modal recognition task, which is crucial to achieving high generalization performance. To address this problem, in this paper, we propose an effective Multi-Modal Local Receptive Field Extreme Learning Machine (MM-LRF-ELM) structure, while maintaining ELM’s advantages of training efficiency. In this structure, LRF-ELM is first conducted for feature extraction for each modality separately. And then, the shared layer is developed by combining these features from each modality. Finally, the Extreme Learning Machine (ELM) is used as supervised feature classifier for the final decision. Experimental validation on Washington RGB-D Object Dataset illustrates that the proposed multiple modality fusion method achieves better recognition performance. [ABSTRACT FROM AUTHOR]

Methanation is the core process of synthetic natural gas, the performance of the entire reaction system depends on precise values of the reaction condition parameters. Accurate predictions of the CO conversion rate of the methanation reaction can eliminate time-consuming and complex steps in experiments and speed up the discovery of the best reaction conditions. However, the methanation reaction is an uncertain, highly complex, and highly nonlinear process. Thus, this paper proposes a machine learning prediction model for the methanation reaction to facilitate the subsequent search for optimal reaction conditions. The reaction temperature, pressure, hydrogen–carbon ratio, water vapor content, CO 2 content, and space velocity were selected as the condition variables. The CO conversion rate was the optimization objective. An extreme learning machine (ELM) was selected as a prediction model. Because the input weights and bias matrices of the ELM are randomly generated, an ELM based on a state transition simulated annealing (STASA-ELM) algorithm is proposed. The STASA algorithm was used to optimize the ELM to improve the accuracy and stability of the model. Five additional sets of experimental data were designed for the experiment, and the error between the experimental and predicted values was small. Thus, the STASA-ELM algorithm can accurately predict the conversion of CO for different values of reaction conditions. [Display omitted] • Methanation reaction conditions were predictively modeled by machine learning. • ELM-STASA optimization improved the stability and accuracy of model prediction. • Very small error was obtained between predicted and experimental results. • Machine learning showed advantages with saving time, labor and material resources. [ABSTRACT FROM AUTHOR]

Point cloud registration precision degrades when the scene contains symmetric structures or repeated patches. Also, the common operation of continuous downsampling in existing registration methods causes the loss of detailed information and leads to further registration precision degradation. In this paper, we propose a high precision point cloud registration network (HPNet) to learn discriminative features by considering both multi-scale information and position information. We first propose a multi-scale feature extraction module similar to the feature pyramid that allows the extracted features to contain multi-scale information. Then, an information interaction is performed on the features to learn global contextual information by using Transformer with a hybrid position encoding, which takes into account both absolute and relative positions of points. Finally, the features obtained from the information interaction module are directly used to predict the point correspondences. Comprehensive experiments on 3DMatch, 3DLoMatch, ModelNet, and ModelLoNet datasets demonstrate the state-of-the-art performance of the proposed method and the implementation of HPNet in SLAM shows its effectiveness in real application. [Display omitted] • Point cloud registration loses detail information during continuous downsampling. • Feature fusion of low-level detail information and high-level semantic information. • Hybrid position encoding consider both absolute and relative positions. positions. • Point cloud registration algorithms applied to SLAM. [ABSTRACT FROM AUTHOR]

[Display omitted] • A series of hydrostyryl pyridinium derivatives with dual-state emission behaviors were employed for synthesis of fluorescent polymers resulting unique fluorescent behaviors. • Significant solvent- and excitation-dependent fluorescent emissions were observed and their multichromatic behaviors caused by enol-to-keto transition of phenol group and aggregation were addressed by using quantum computation. To synthesize fluorescent polymers, hydrostyryl pyridinium derivatives were methacrylated and afforded monomers of MA-SP-OH-X (X = NO 2 , F, substituted group on phenyl ring) followed by free-radically homo- and copolymerized with methyl methacrylate (MMA), butyl acrylate (BA) and styrene (St), respectively. The resulting copolymers showed green, orange and red emissions with significant multichromatic behaviors and solvent- and excitation-dependence. Moreover, P(BA- co -MA-SP-OH-NO 2) behaved as a thermal elastomer and showed high stretchability and self-heal performance. In addition, with the copolymers as macro-additives, fluorescent poly(vinylidene difluoride) (PVDF) films were fabricated by using electrospinning technique. The unexpected multichromatic fluorescent behaviors showed by the copolymers were attributed to the enol-to-keto transition of phenol group in SP-OH moiety and significant aggregation of fluorophore moieties. [ABSTRACT FROM AUTHOR]