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Content-based image retrieval algorithm (CBIR) aims to find semantically matching or similar images with query images. It analyzes visual content in a large number of image databases. It is important to obtain discriminant image representation by feature extraction. With the continuous development of deep learning, the image feature representation method used in image retrieval has gradually changed. The original extraction method is based on manual features. Now it is based on deep features. From the perspective of different feature extraction methods, the recent image retrieval algorithms based on depth feature are reviewed and traced. The image retrieval algorithms based on depth feature are divided into two aspects: depth global feature and depth local feature. The deep convolution feature aggregation technique is emphasized in the deep local feature algorithm. The widely used image retrieval methods of deep global and local feature fusion are summarized. This paper discusses the practical application of deep feature image retrieval technology in remote sensing image retrieval, e-commerce product retrieval and medical image retrieval. And it compares the performance of these feature extraction algorithms in image retrieval accuracy. Finally, the future research trend of depth feature extraction in case image retrieval is forecasted.

With the rapid development of related technologies in the field of natural language processing, as an upstream task of natural language processing, improving the accuracy of named entity recognition is of great significance for subsequent text processing tasks. However, due to the differences between Chinese and English languages, it is difficult to transfer the research results of English named entity recognition into Chinese research effectively. Therefore, the key issues in the current research of Chinese named entity recognition are analyzed from the following four aspects: Firstly, the development of named entity recognition is taken as the main clue, the advantages and disadvantages, common methods and research results of each stage are comprehensively discussed. Secondly, the Chinese text preprocessing methods are summarized from the perspective of sequence annotation, evaluation index, Chinese word segmentation methods and datasets. Then, aiming at the Chinese character and word feature fusion method, the current research is summarized from the perspective of character fusion and word fusion, and the optimization direction of the current Chinese named entity recognition model is discussed. Finally, the practical applications of Chinese named entity recognition in various fields are analyzed. This paper discusses the current research on Chinese named entity recognition, aiming to help researchers understand the research direction and significance of this task more comprehensively, so as to provide a certain reference for proposing new methods and new improvements.

Background The prevalence of age-related cognitive impairment, including dementia, has significantly increased with population aging. It has been shown that cognitive function is associated with gait status. Previously, researchers used statistical analysis methods instead of machine learning methods to study the gait of amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD) . Objective To develop a model to identify aMCI and AD based on gait status using machine learning methods, explore gait markers differentiating between aMCI and AD, and to assess their possible values as aided tools in diagnosing aMCI and AD. Methods We recruited 102 cases from the Rehabilitation Hospital Affiliated to National Research Center for Rehabilitation Technical Aids, the First People's Hospital of Foshan, and Affiliated Zhongshan Hospital of Dalian University from December 2018 to December 2020, and included 98 of them according to the screening criteria, including 55 patients with aMCI, 10 patients with AD, and 33 healthy controls (HC) . The gait parameters of the participants were collected during performing single-task (free walking) , dual-task (counting backwards in sevens) and another dual-task (counting backwards from 100) using a wearable device. Random forest (RF) algorithm and gradient boosting decision tree (GBDT) algorithm were separately used to establish a model to compare the effect of two algorithms in recognizing three groups, with 10 gait parameters as predictive variables and the physical status (healthy, aMCI, AD) as response variables. Then important features were chosen using a machine learning algorithm combined with recursive feature elimination (RFE) . Results No statistically significant differences were found among the three groups in terms of sex ratio, average age, height, body weight or shoe size (P>0.05) , while the differences in terms of average MMSE score and MoCA score were statistically significant (P

Triggering the lattice oxygen oxidation mechanism is crucial for improving oxygen evolution reaction (OER) performance, because it could bypass the scaling relation limitation associated with the conventional adsorbate evolution mechanism through the direct formation of oxygen–oxygen bond. High‐valence transition metal sites are favorable for activating the lattice oxygen, but the deep oxidation of pre‐catalysts suffers from a high thermodynamic barrier. Here, taking advantage of the Jahn–Teller (J–T) distortion induced structural instability, we incorporate high‐spin Mn3+ (t2g3eg1 ${{t}_{2g}^{3}{e}_{g}^{1}}$) dopant into Co4N. Mn dopants enable a surface structural transformation from Co4N to CoOOH, and finally to CoO2, as observed by various in situ spectroscopic investigations. Furthermore, the reconstructed surface on Mn‐doped Co4N triggers the lattice oxygen activation, as evidenced experimentally by pH‐dependent OER, tetramethylammonium cation adsorption and online electrochemical mass spectrometry measurements of 18O‐labelled catalysts. In general, this work not only offers the introducing J–T effect approach to regulate the structural transition, but also provides an understanding about the influence of the catalyst's electronic configuration on determining the reaction route, which may inspire the design of more efficient catalysts with activated lattice oxygen. [ABSTRACT FROM AUTHOR]

There are various indoor fingerprint localization techniques utilizing the similarity of received signal strength (RSS) to discriminate the similarity of positions. However, due to the varied states of different wireless access points (APs), each AP's contribution to RSS similarity varies, which affects the accuracy of localization. In our study, we analyzed several critical causes that affect APs' contribution, including APs' health states and APs' positions. Inspired by these insights, for a large-scale indoor space with ubiquitous APs, a threshold was set for all sample RSS to eliminate the abnormal APs dynamically, a correction quantity for each RSS was provided by the distance between the AP and the sample position to emphasize closer APs, and a priority weight was designed by RSS differences (RSSD) to further optimize the capability of fingerprint distances (FDs, the Euclidean distance of RSS) to discriminate physical distance (PDs, the Euclidean distance of positions). Integrating the above policies for the classical WKNN algorithm, a new indoor fingerprint localization technique is redefined, referred to as FDs' discrimination capability improvement WKNN (FDDC-WKNN). Our simulation results showed that the correlation and consistency between FDs and PDs are well improved, with the strong correlation increasing from 0 to 76% and the high consistency increasing from 26% to 99%, which confirms that the proposed policies can greatly enhance the discrimination capabilities of RSS similarity. We also found that abnormal APs can cause significant impact on FDs discrimination capability. Further, by implementing the FDDC-WKNN algorithm in experiments, we obtained the optimal K value in both the simulation scene and real library scene, under which the mean errors have been reduced from 2.2732 m to 1.2290 m and from 4.0489 m to 2.4320 m, respectively. In addition, compared to not using the FDDC-WKNN, the cumulative distribution function (CDF) of the localization errors curve converged faster and the error fluctuation was smaller, which demonstrates the FDDC-WKNN having stronger robustness and more stable localization performance. [ABSTRACT FROM AUTHOR]

The separation of CO activation and C─C coupling enables oxide–zeolite catalyzed tandem reaction toward direct conversion of syngas into light olefins to be a promising alternative for the traditional Fischer–Tropsch process with elevated selectivity. Utilizing controlled mass transfer to match the activation of reactants and the coupling of intermediates presents an appealing strategy to break the limitation on space‐time yield in this tandem process. In this work, ZnCrOx hollow multi‐shelled structures (HoMS) are fabricated to enhance the adsorption and enrichment ability of syngas, thereby strengthening the rate‐determining activation step of reactants and resulting in an accelerated generation of intermediates. When coupled with SAPO zeolite, quintuple‐shelled ZnCrOx HoMS‐based catalysts exhibit a record high C2= ‐C4= space‐time yield of 559 mg·gcat−1·h−1 with a selectivity of 90%. This work demonstrates the advantages of precise nanostructure sculpturing on the mass transfer behavior in the microenvironment and provides a new strategy for highly selective direct conversion of syngas to light olefins, based on efficient activation of reactants and sufficient feeding of intermediates through enrichment effects within HoMS. [ABSTRACT FROM AUTHOR]