A series of stable heterometallic Fe2M cluster‐based MOFs (NNU‐31‐M, M=Co, Ni, Zn) photocatalysts are presented. They can achieve the overall conversion of CO2 and H2O into HCOOH and O2 without the ...assistance of additional sacrificial agent and photosensitizer. The heterometallic cluster units and photosensitive ligands excited by visible light generate separated electrons and holes. Then, low‐valent metal M accepts electrons to reduce CO2, and high‐valent Fe uses holes to oxidize H2O. This is the first MOF photocatalyst system to finish artificial photosynthetic full reaction. It is noted that NNU‐31‐Zn exhibits the highest HCOOH yield of 26.3 μmol g−1 h−1 (selectivity of ca. 100 %). Furthermore, the DFT calculations based on crystal structures demonstrate the photocatalytic reaction mechanism. This work proposes a new strategy for how to design crystalline photocatalyst to realize artificial photosynthetic overall reaction.
A series of stable heterometallic Fe2M cluster‐based MOFs achieve the overall conversion of CO2 and H2O into HCOOH and O2 without the assistance of additional sacrificial agent and photosensitizer. A strategy is proposed to design crystalline photocatalysts to realize the overall artificial photosynthetic reaction.
The prognostic role of inflammation index like neutrophil‐to‐lymphocyte ratio (NLR) in colorectal cancer (CRC) remains controversial. We conduct a meta‐analysis to determine the predictable value of ...NLR in the clinical outcome of CRC patients. The analysis was carried out based on the data from 16 studies (19 cohorts) to evaluate the association between NLR and overall survival (OS) and progression‐free survival (PFS) in patients with CRC. In addition, the relationship between NLR and clinicopathological parameters was assessed. Hazard ratio (HR) or odds ratio (OR) with its 95% confidence interval (CI) was used as the effect size estimate. Our analysis results indicated that elevated pretreatment NLR predicted poorer OS (HR: 1.813, 95% CI: 1.499–2.193) and PFS (HR: 2.102, 95% CI: 1.554–2.843) in patients with CRC. Increased NLR is also significantly associated with the poorer differentiation of the tumor (OR: 1.574, 95% CI: 1.226–2.022) and higher carcino‐embryonie antigen (CEA) level (OR: 1.493, 95% CI: 1.308–1.705). By these results, we conclude that NLR gains a prognostic value for patients with CRC. NLR should be monitored in CRC patients for rational stratification of the patients and adjusting the treatment strategy.
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Although correlated with the severity of disease course, the impact of neutrophil‐to‐lymphocyte ratio (NLR) on survival and tumor characteristics in cancer patients remains unclear. In the meta‐analysis presented here, elevated pre‐treatment NLR was found to predict poor overall survival in patients with colorectal cancer. NLR was also associated with unfavorable biologic behavior in colorectal cancer. The findings suggest that NLR, as an inexpensive and widely available index, should be routinely monitored in colorectal cancer patients.
In recent years, the boundaries between e-commerce and social networking have become increasingly blurred. Many e-commerce Web sites support the mechanism of social login where users can sign on the ...Web sites using their social network identities such as their Facebook or Twitter accounts. Users can also post their newly purchased products on microblogs with links to the e-commerce product Web pages. In this paper, we propose a novel solution for cross-site cold-start product recommendation, which aims to recommend products from e-commerce Web sites to users at social networking sites in "cold-start" situations, a problem which has rarely been explored before. A major challenge is how to leverage knowledge extracted from social networking sites for cross-site cold-start product recommendation. We propose to use the linked users across social networking sites and e-commerce Web sites (users who have social networking accounts and have made purchases on e-commerce Web sites) as a bridge to map users' social networking features to another feature representation for product recommendation. In specific, we propose learning both users' and products' feature representations (called user embeddings and product embeddings, respectively) from data collected from e-commerce Web sites using recurrent neural networks and then apply a modified gradient boosting trees method to transform users' social networking features into user embeddings. We then develop a feature-based matrix factorization approach which can leverage the learnt user embeddings for cold-start product recommendation. Experimental results on a large dataset constructed from the largest Chinese microblogging service Sina Weibo and the largest Chinese B2C e-commerce website JingDong have shown the effectiveness of our proposed framework.
Due to the rapid technological development of various sensors, a huge volume of high spatial resolution (HSR) image data can now be acquired. How to efficiently recognize the scenes from such HSR ...image data has become a critical task. Conventional approaches to remote sensing scene classification only utilize information from HSR images. Therefore, they always need a large amount of labeled data and cannot recognize the images from an unseen scene class without any visual sample in the labeled data. To overcome this drawback, we propose a novel approach for recognizing images from unseen scene classes, i.e., zero-shot scene classification (ZSSC). In this approach, we first use the well-known natural language process model, word2vec, to map names of seen/unseen scene classes to semantic vectors. A semantic-directed graph is then constructed over the semantic vectors for describing the relationships between unseen classes and seen classes. To transfer knowledge from the images in seen classes to those in unseen classes, we make an initial label prediction on test images by an unsupervised domain adaptation model. With the semantic-directed graph and initial prediction, a label-propagation algorithm is then developed for ZSSC. By leveraging the visual similarity among images from the same scene class, a label refinement approach based on sparse learning is used to suppress the noise in the zero-shot classification results. Experimental results show that the proposed approach significantly outperforms the state-of-the-art approaches in ZSSC.
AbstractObjectiveTo examine the protective effects of appropriate personal protective equipment for frontline healthcare professionals who provided care for patients with coronavirus disease 2019 ...(covid-19).DesignCross sectional study.SettingFour hospitals in Wuhan, China.Participants420 healthcare professionals (116 doctors and 304 nurses) who were deployed to Wuhan by two affiliated hospitals of Sun Yat-sen University and Nanfang Hospital of Southern Medical University for 6-8 weeks from 24 January to 7 April 2020. These study participants were provided with appropriate personal protective equipment to deliver healthcare to patients admitted to hospital with covid-19 and were involved in aerosol generating procedures. 77 healthcare professionals with no exposure history to covid-19 and 80 patients who had recovered from covid-19 were recruited to verify the accuracy of antibody testing.Main outcome measuresCovid-19 related symptoms (fever, cough, and dyspnoea) and evidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, defined as a positive test for virus specific nucleic acids in nasopharyngeal swabs, or a positive test for IgM or IgG antibodies in the serum samples.ResultsThe average age of study participants was 35.8 years and 68.1% (286/420) were women. These study participants worked 4-6 hour shifts for an average of 5.4 days a week; they worked an average of 16.2 hours each week in intensive care units. All 420 study participants had direct contact with patients with covid-19 and performed at least one aerosol generating procedure. During the deployment period in Wuhan, none of the study participants reported covid-19 related symptoms. When the participants returned home, they all tested negative for SARS-CoV-2 specific nucleic acids and IgM or IgG antibodies (95% confidence interval 0.0 to 0.7%).ConclusionBefore a safe and effective vaccine becomes available, healthcare professionals remain susceptible to covid-19. Despite being at high risk of exposure, study participants were appropriately protected and did not contract infection or develop protective immunity against SARS-CoV-2. Healthcare systems must give priority to the procurement and distribution of personal protective equipment, and provide adequate training to healthcare professionals in its use.
Graphene nanosheets were prepared by complete oxidation of pristine graphite followed by thermal exfoliation and reduction. Polyethylene terephthalate (PET)/graphene nanocomposites were prepared by ...melt compounding. Transmission electron microscopy observation indicated that graphene nanosheets exhibited a uniform dispersion in PET matrix. The incorporation of graphene greatly improved the electrical conductivity of PET, resulting in a sharp transition from electrical insulator to semiconductor with a low percolation threshold of 0.47vol.%. A high electrical conductivity of 2.11S/m was achieved with only 3.0vol.% of graphene. The low percolation threshold and superior electrical conductivity are attributed to the high aspect ratio, large specific surface area and uniform dispersion of the graphene nanosheets in PET matrix.
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Image annotation aims to annotate a given image with a variable number of class labels corresponding to diverse visual concepts. In this paper, we address two main issues in large-scale image ...annotation: 1) how to learn a rich feature representation suitable for predicting a diverse set of visual concepts ranging from object, scene to abstract concept and 2) how to annotate an image with the optimal number of class labels. To address the first issue, we propose a novel multi-scale deep model for extracting rich and discriminative features capable of representing a wide range of visual concepts. Specifically, a novel two-branch deep neural network architecture is proposed, which comprises a very deep main network branch and a companion feature fusion network branch designed for fusing the multi-scale features computed from the main branch. The deep model is also made multi-modal by taking noisy user-provided tags as model input to complement the image input. For tackling the second issue, we introduce a label quantity prediction auxiliary task to the main label prediction task to explicitly estimate the optimal label number for a given image. Extensive experiments are carried out on two large-scale image annotation benchmark datasets, and the results show that our method significantly outperforms the state of the art.
The fundamental goal of artificial intelligence (AI) is to mimic the core cognitive activities of human. Despite tremendous success in the AI research, most of existing methods have only ...single-cognitive ability. To overcome this limitation and take a solid step towards artificial general intelligence (AGI), we develop a foundation model pre-trained with huge multimodal data, which can be quickly adapted for various downstream cognitive tasks. To achieve this goal, we propose to pre-train our foundation model by self-supervised learning with weak semantic correlation data crawled from the Internet and show that promising results can be obtained on a wide range of downstream tasks. Particularly, with the developed model-interpretability tools, we demonstrate that strong imagination ability is now possessed by our foundation model. We believe that our work makes a transformative stride towards AGI, from our common practice of "weak or narrow AI" to that of "strong or generalized AI".
In this study, we proposed that homo superalkali NM4 clusters with high tetrahedral geometry, can be applied to develop high-performance hydrogen storage materials. Moreover, their special bonding ...structures and chemical stability make them ideal units for decoration of different kinds of pristine monolayers. We made a trial to decorate the NLi4 clusters onto the 1D graphene nanoribbon, and employed density functional theory (DFT) computational studies to solve its electronic structure, and further evaluate its applicability in hydrogen storage. We found that the electronic charges on Li atoms were successfully transferred to the pristine monolayer, thus a partial electronic field around each Li atom was formed. This subsequently leads to the polarization of the adsorbed hydrogen molecules, and further enhances the electrostatic interactions between the Li atoms and hydrogen. Each NLi4 cluster can adsorb at most 16 hydrogen molecules. For this novel material, its total capacity of hydrogen storage can reach to 11.2 wt %, surpassing the target value of 5.5 wt %, set by the U.S department of energy (DOE) 1, making itself an ideal unit for advanced energy materials design.
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•Superalkali NLi4 was applied to decorate graphene nanoribbon.•The NLi4-decorated graphene nanoribbon performs well in H2 adsorption.•Binding structure of this material was solved via DFT calculations.
Searching Trajectories by Regions of Interest Shuo Shang; Lisi Chen; Jensen, Christian S. ...
IEEE transactions on knowledge and data engineering,
07/2017, Letnik:
29, Številka:
7
Journal Article
Recenzirano
Odprti dostop
With the increasing availability of moving-object tracking data, trajectory search is increasingly important. We propose and investigate a novel query type named trajectory search by regions of ...interest (TSR query). Given an argument set of trajectories, a TSR query takes a set of regions of interest as a parameter and returns the trajectory in the argument set with the highest spatial-density correlation to the query regions. This type of query is useful in many popular applications such as trip planning and recommendation, and location based services in general. TSR query processing faces three challenges: how to model the spatial-density correlation between query regions and data trajectories, how to effectively prune the search space, and how to effectively schedule multiple so-called query sources. To tackle these challenges, a series of new metrics are defined to model spatial-density correlations. An efficient trajectory search algorithm is developed that exploits upper and lower bounds to prune the search space and that adopts a query-source selection strategy, as well as integrates a heuristic search strategy based on priority ranking to schedule multiple query sources. The performance of TSR query processing is studied in extensive experiments based on real and synthetic spatial data.
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