675 research outputs found

    Machine learning-based characterisation of urban morphology with the street pattern

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    Streets are a crucial part of the built environment, and their layouts, the street patterns, are widely researched and contribute to a quantitative understanding of urban morphology. However, traditional street pattern analysis only considers a few broadly defined characteristics. It uses administrative boundaries and grids as units of analysis that fail to encompass the diversity and complexity of street networks. To address these challenges, this research proposes a machine learning-based approach to automatically recognise street patterns that employs an adaptive analysis unit based on street-based local areas (SLAs). SLAs use a network partitioning technique that can adapt to distinct street networks, making it particularly suitable for different urban contexts. By calculating several streets’ network metrics and performing a hierarchical clustering method, streets with similar characters are grouped under the same street pattern. A case study is carried out in six cities worldwide. The results show that street pattern types are rather diverse and hierarchical, and categorising them into clearly demarcated taxonomy is challenging. The study derives a set of new morphometrics-based street patterns with four major types that resemble conventional street patterns and eleven sub-types to significantly increase their diversity for broader coverage of urban morphology. The new patterns capture urban structural differences across cities, such as the urban-suburban division and the number of urban centres present. In conclusion, the proposed machine learning-based morphometric street pattern to characterise urban morphology has an enhanced ability to encompass more information from the built environment while maintaining the intuitiveness of using patterns

    Insight into the Antibiotic Resistance of Bacteria Isolated from Popular Aquatic Products Collected in Zhejiang, China

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    The present study was aimed to obtain a close insight into the distribution and diversity of antibiotic-resistant bacteria (ARB) and antibiotic-resistance genes (ARGs) among the aquatic products collected in Zhejiang, China. A total of 136 presumptive ARB picked up from six aquatic samples were classified into 22 genera and 49 species based on the 16S rDNA sequencing. Aeromonas spp., Shewanella spp., Acinetobacter spp., Myroides spp., Pseudomonas spp., and Citrobacter spp. accounted for 80% of the ARB. Among them, 109 isolates (80.15%) exhibited resistance to at least one antibiotic. Most isolates showed resistance to not only the originally selected drug but also to one to three other tested drugs. The diversity of ARB distributed in different aquatic products was significant. Furthermore, the resistance data obtained from genotypic tests were not entirely consistent with the results of the phenotypic evaluation. The genes qnrS, tetA, floR, and cmlA were frequently detected in their corresponding phenotypic resistant isolates. In contrast, the genes sul2, aac(6’)-Ib, and blaPSE were less frequently found in the corresponding phenotypically resistant strains. The high diversity and detection rate of ARB and ARGs in aquaculture might be a significant threat to the food chains closely related to human health

    Highly-Entangled Polyradical Nanographene with Coexisting Strong Correlation and Topological Frustration

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    Open-shell benzenoid polycyclic aromatic hydrocarbons, known as magnetic nanographenes, exhibit unconventional π-magnetism arising from topological frustration or strong electronic-electron interaction. Imprinting multiple strongly entangled spins into polyradical nanographenes creates a major paradigm shift in realizing non-trivial collective quantum behaviors and exotic quantum phases in organic quantum materials. However, conventional design approaches are limited by a single magnetic origin, which can restrict the number of correlated spins or the type of magnetic ordering in open-shell nanographenes. Here, we present a novel design strategy combing topological frustration and electron-electron interactions to fabricate the largest fully-fused open-shell nanographene reported to date, a 'butterfly'-shaped tetraradical on Au(111). We employed bond-resolved scanning tunneling microscopy and spin excitation spectroscopy to unambiguously resolve the molecular backbone and reveal the strongly correlated open-shell character, respectively. This nanographene contains four unpaired electrons with both ferromagnetic and anti-ferromagnetic interactions, harboring a many-body singlet ground state and strong multi-spin entanglement, which can be well described by many-body calculations. Furthermore, we demonstrate that the nickelocene magnetic probe can sense highly-correlated spin states in nanographene. The ability to imprint and characterize many-body strongly correlated spins in polyradical nanographenes not only presents exciting opportunities for realizing non-trivial quantum magnetism and phases in organic materials but also paves the way toward high-density ultrafast spintronic devices and quantum information technologies

    Efficient Serverless Function Scheduling at the Network Edge

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    Serverless computing is a promising approach for edge computing since its inherent features, e.g., lightweight virtualization, rapid scalability, and economic efficiency. However, previous studies have not studied well the issues of significant cold start latency and highly dynamic workloads in serverless function scheduling, which are exacerbated at the resource-limited network edge. In this paper, we formulate the Serverless Function Scheduling (SFS) problem for resource-limited edge computing, aiming to minimize the average response time. To efficiently solve this intractable scheduling problem, we first consider a simplified offline form of the problem and design a polynomial-time optimal scheduling algorithm based on each function's weight. Furthermore, we propose an Enhanced Shortest Function First (ESFF) algorithm, in which the function weight represents the scheduling urgency. To avoid frequent cold starts, ESFF selectively decides the initialization of new function instances when receiving requests. To deal with dynamic workloads, ESFF judiciously replaces serverless functions based on the function weight at the completion time of requests. Extensive simulations based on real-world serverless request traces are conducted, and the results show that ESFF consistently and substantially outperforms existing baselines under different settings

    From Wide to Deep: Dimension Lifting Network for Parameter-efficient Knowledge Graph Embedding

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    Knowledge graph embedding (KGE) that maps entities and relations into vector representations is essential for downstream applications. Conventional KGE methods require high-dimensional representations to learn the complex structure of knowledge graph, but lead to oversized model parameters. Recent advances reduce parameters by low-dimensional entity representations, while developing techniques (e.g., knowledge distillation or reinvented representation forms) to compensate for reduced dimension. However, such operations introduce complicated computations and model designs that may not benefit large knowledge graphs. To seek a simple strategy to improve the parameter efficiency of conventional KGE models, we take inspiration from that deeper neural networks require exponentially fewer parameters to achieve expressiveness comparable to wider networks for compositional structures. We view all entity representations as a single-layer embedding network, and conventional KGE methods that adopt high-dimensional entity representations equal widening the embedding network to gain expressiveness. To achieve parameter efficiency, we instead propose a deeper embedding network for entity representations, i.e., a narrow entity embedding layer plus a multi-layer dimension lifting network (LiftNet). Experiments on three public datasets show that by integrating LiftNet, four conventional KGE methods with 16-dimensional representations achieve comparable link prediction accuracy as original models that adopt 512-dimensional representations, saving 68.4% to 96.9% parameters

    Fractured morphology of femoral head associated with subsequent femoral neck fracture: Injury analyses of 2D and 3D models of femoral head fractures with computed tomography

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    Background: The injury of femoral head varies among femoral head fractures (FHFs). In addition, the injury degree of the femoral head is a significant predictor of femoral neck fracture (FNF) incidence in patients with FHFs. However, the exact measurement methods have yet been clearly defined based on injury models of FHFs. This study aimed to design a new measurement for the injury degree of the femoral head on 2D and 3D models with computed tomography (CT) images and investigate its association with FHFs with FNF.Methods: A consecutive series of 209 patients with FHFs was assessed regarding patient characteristics, CT images, and rate of FNF. New parameters for injury degree of femoral head, including percentage of maximum defect length (PMDL) in the 2D CT model and percentage of fracture area (PFA) in the 3D CT-reconstruction model, were respectively measured. Four 2D parameters included PMDLs in the coronal, cross-sectional and sagittal plane and average PMDL across all three planes. Reliability tests for all parameters were evaluated in 100 randomly selected patients. The PMDL with better reliability and areas under curves (AUCs) was finally defined as the 2D parameter. Factors associated with FNF were determined by binary logistic regression analysis. The sensitivity, specificity, likelihood ratios, and positive and negative predictive values for different cut-off values of the 2D and 3D parameters were employed to test the diagnostic accuracy for FNF prediction.Results: Intra- and inter-class coefficients for all parameters were ≥0.887. AUCs of all parameters ranged from 0.719 to 0.929 (p < 0.05). The average PMDL across all three planes was defined as the 2D parameter. The results of logistic regression analysis showed that average PMDL across all three planes and PFA were the significant predictors of FNF (p < 0.05). The cutoff values of the average PMDL across all three planes and PFA were 91.65% and 29.68%. The sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, predictive positive value and negative predictive value of 2D (3D) parameters were 91.7% (83.3%), 93.4% (58.4%), 13.8 (2.0), 0.09 (0.29), 45.83% (10.87%), and 99.46% (98.29%).Conclusion: The new measurement on 2D and 3D injury models with CT has been established to assess the fracture risk of femoral neck in patients with FHFs in the clinic practice. 2D and 3D parameters in FHFs were a feasible adjunctive diagnostic tool in identifying FNFs. In addition, this finding might also provide a theoretic basis for the investigation of the convenient digital-model in complex injury analysis

    Self-adaptive amorphous CoOxCly electrocatalyst for sustainable chlorine evolution in acidic brine

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    Electrochemical chlorine evolution reaction is of central importance in the chlor-alkali industry, but the chlorine evolution anode is largely limited by water oxidation side reaction and corrosion-induced performance decay in strong acids. Here we present an amorphous CoOxCly catalyst that has been deposited in situ in an acidic saline electrolyte containing Co2+ and Cl- ions to adapt to the given electrochemical condition and exhibits ~100% chlorine evolution selectivity with an overpotential of ~0.1 V at 10 mA cm−2 and high stability over 500 h. In situ spectroscopic studies and theoretical calculations reveal that the electrochemical introduction of Cl- prevents the Co sites from charging to a higher oxidation state thus suppressing the O-O bond formation for oxygen evolution. Consequently, the chlorine evolution selectivity has been enhanced on the Cl-constrained Co-O* sites via the Volmer-Heyrovsky pathway. This study provides fundamental insights into how the reactant Cl-itself can work as a promoter toward enhancing chlorine evolution in acidic brine

    Set-Based Face Recognition Beyond Disentanglement: Burstiness Suppression With Variance Vocabulary

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    Set-based face recognition (SFR) aims to recognize the face sets in the unconstrained scenario, where the appearance of same identity may change dramatically with extreme variances (e.g., illumination, pose, expression). We argue that the two crucial issues in SFR, the face quality and burstiness, are both identity-irrelevant and variance-relevant. The quality and burstiness assessment are interfered with by the entanglement of identity, and the face recognition is interfered with by the entanglement of variance. Thus we propose to separate the identity features with the variance features in a light-weighted set-based disentanglement framework. Beyond disentanglement, the variance features are fully utilized to indicate face quality and burstiness in a set, rather than being discarded after training. To suppress face burstiness in the sets, we propose a vocabulary-based burst suppression (VBS) method which quantizes faces with a reference vocabulary. With interword and intra-word normalization operations on the assignment scores, the face burtisness degrees are appropriately estimated. The extensive illustrations and experiments demonstrate the effect of the disentanglement framework with VBS, which gets new state-of-the-art on the SFR benchmarks. The code will be released at https://github.com/Liubinggunzu/set_burstiness.Comment: ACM MM 2022 accepted, code will be release

    Research on High-Speed Catamaran Motion Reduction with Semi-Active Control of Flexible Pontoon

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    A high-speed catamaran with a suspension system and flexible pontoons to reduce motion is proposed, and the vertical motion characteristics of the vessel are investigated. The results demonstrate that altering the stiffness of the flexible pontoon can significantly alter the motion characteristics of a high-speed vessel when subjected to wave excitation. The maximum relative error between the theoretical and experimental values of the vertical dynamic characteristics of the flexible pontoon, considered as a gas spring, is 10.5%. The vertical force exerted by the pontoon exhibits nonlinear behavior in response to compression, yet displays approximately linear behavior within its primary operational range. The design of the Linear Quadratic Regulator controller, utilizing genetic algorithm optimization, avoids the issue of subjectively setting weight coefficients typically found in traditional control systems. This approach achieves the objective of determining the optimal feedback matrix within specified constraints. Simulation results illustrate that the LQR controller developed using genetic algorithm significantly enhance the semi-active suspension performance compared to the passive suspension system. The Root Mean Square value of the main cabin acceleration is reduced by 85.82%, simultaneously reducing the RMS value of the suspension dynamic travel by 85.03% and the RMS value of the pontoon dynamic displacement by 24.42%. These outcomes thoroughly substantiate the effective reduction in vertical motion, effectively attenuating the motion of high-speed vessels under wave excitation

    Current status and focus of breast reconstruction research in China and abroad: a bibliometric study

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    Background and purpose: According to the 2020 global cancer burden data of the International Agency for Research on Cancer (IARC), breast cancer has replaced lung cancer as the most common type of cancer worldwide. The aim of this study was to conduct a bibliometric and visual analysis of breast reconstruction related research in China and abroad published in the past 5 years, to understand the research status and development trend in this field, to discuss the focus of research in different countries and different disciplines, and to provide reference for other researchers. Methods: Relevant literatures about breast reconstruction were retrieved from the Web of Science Core Collection. The VOS viewer 1.6.15 software was used to extract the authors, countries, institutions and keywords to generate network maps of high-yield authors, institutions and high-frequency keywords clustering network. Results: A total of 4 815 documents meeting the requirements were retrieved, which showed an upward trend in the past five years. Regarding the discipline, 838 documents (17.40%) were published by breast surgery and Cancer Surgery, 3 308 (68.70%) were published by plastic surgery, and 669 (13.90%) were jointly published by both types of researchers. There were differences in the disciplines of the main authors between China and abroad. In China, authors from breast surgery published a larger proportion of documents (138, 44.52%), while the number of documents published by authors of plastic surgery (129, 44.52%) and the joint publication of both types of authors (43, 13.87%) was relatively small. However, foreign documents mainly came from authors of plastic surgery (74.74%). There were more cooperative groups (155) formed by major foreign authors, and more frequent joint publishing between groups, while Chinese authors formed only 16 cooperative groups with less cooperation. Authors from breast surgery focused more on oncology-related issues in breast reconstruction, while in plastic surgery, more attentions were paid on autologous tissue reconstruction. Conclusion: Breast reconstruction has gradually attracted the attention of Chinese and foreign researchers. Compared with foreign countries, there were problems such as lack of high-quality research and less cooperative research in China. There were differences in the research focus of breast reconstruction between China and abroad, which was mainly related to the differences in the disciplines of researchers
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