Automatic Structural Scene Digitalization

In this paper, we present an automatic system for the analysis and labeling of structural scenes, floor plan drawings in Computer-aided Design (CAD) format. The proposed system applies a fusion strategy to detect and recognize various components of CAD floor plans, such as walls, doors, windows and other ambiguous assets. Technically, a general rule-based filter parsing method is fist adopted to extract effective information from the original floor plan. Then, an image-processing based recovery method is employed to correct information extracted in the first step. Our proposed method is fully automatic and real-time. Such analysis system provides high accuracy and is also evaluated on a public website that, on average, archives more than ten thousands effective uses per day and reaches a relatively high satisfaction rate.Comment: paper submitted to PloS On

Retraction: the “other face” of research collaboration?

The last two decades have witnessed the rising prevalence of both co-publishing and retraction. Focusing on research collaboration, this paper utilizes a unique dataset to investigate factors contributing to retraction probability and elapsed time between publication and retraction. Data analysis reveals that the majority of retracted papers are multi-authored and that repeat offenders are collaboration prone. Yet, all things being equal, collaboration, in and of itself, does not increase the likelihood of producing flawed or fraudulent research, at least in the form of retraction. That holds for all retractions and also retractions due to falsification, fabrication, and plagiarism (FFP). The research also finds that publications with authors from elite universities are less likely to be retracted, which is particularly true for retractions due to FFP. China stands out with the fastest retracting speed compared to other countries. Possible explanations, limitations, and policy implications are also discussed

Simulating the Integration of Urban Air Mobility into Existing Transportation Systems: A Survey

Urban air mobility (UAM) has the potential to revolutionize transportation in metropolitan areas, providing a new mode of transportation that could alleviate congestion and improve accessibility. However, the integration of UAM into existing transportation systems is a complex task that requires a thorough understanding of its impact on traffic flow and capacity. In this paper, we conduct a survey to investigate the current state of research on UAM in metropolitan-scale traffic using simulation techniques. We identify key challenges and opportunities for the integration of UAM into urban transportation systems, including impacts on existing traffic patterns and congestion; safety analysis and risk assessment; potential economic and environmental benefits; and the development of shared infrastructure and routes for UAM and ground-based transportation. We also discuss the potential benefits of UAM, such as reduced travel times and improved accessibility for underserved areas. Our survey provides a comprehensive overview of the current state of research on UAM in metropolitan-scale traffic using simulation and highlights key areas for future research and development

LSGNN: Towards General Graph Neural Network in Node Classification by Local Similarity

Heterophily has been considered as an issue that hurts the performance of Graph Neural Networks (GNNs). To address this issue, some existing work uses a graph-level weighted fusion of the information of multi-hop neighbors to include more nodes with homophily. However, the heterophily might differ among nodes, which requires to consider the local topology. Motivated by it, we propose to use the local similarity (LocalSim) to learn node-level weighted fusion, which can also serve as a plug-and-play module. For better fusion, we propose a novel and efficient Initial Residual Difference Connection (IRDC) to extract more informative multi-hop information. Moreover, we provide theoretical analysis on the effectiveness of LocalSim representing node homophily on synthetic graphs. Extensive evaluations over real benchmark datasets show that our proposed method, namely Local Similarity Graph Neural Network (LSGNN), can offer comparable or superior state-of-the-art performance on both homophilic and heterophilic graphs. Meanwhile, the plug-and-play model can significantly boost the performance of existing GNNs. Our code is provided at https://github.com/draym28/LSGNN.Comment: The first two authors contributed equally to this work; IJCAI2

Bank Credit Strategy Model Based on AHP-Fuzzy Comprehensive Evaluation

Credit risk control and credit strategy formulation of medium and micro enterprises have always been important strategic issues faced by commercial banks. Banks usually make corporate loan policies based on the credit degree, the information of trading bills and the relationship of supply-demand chain of the enterprise. In this paper, we established the AHP-Fuzzy comprehensive evaluation model for quantifying enterprise credit risk. Based on the relevant data of 123 enterprises with credit records, the credit strategy is formulated according to the three indicators of enterprise strength, enterprise reputation and stability of supply-demand relationship. This paper also combines the credit reputation, credit risk and supply and demand stability rating in order to establish the bank credit strategic planning model to decide whether to lend or not and the lending order. The conclusion shows that, under the condition of constant total loan amount, the enterprises with the highest credit rating should be given priority. Then, combined with the change of customer turnover rate with interest rate, we take the bank's maximize expected income as objective to calculate the optimal loan interest rate of different customer groups

Experimental and numerical investigation of fractal-tree-like heat exchanger manufactured by 3D printing

© 2018 Elsevier Ltd The manufacturing difficulties of complex fractal-tree-like heat exchangers have limited their industrial applications, although many evidences have shown that they have significant advantages in heat transfer. Nevertheless, the emerging 3D printing technology has brought great opportunity for the development of complex structured device. In the present study, three-dimensional (3D) fractal-tree-like heat exchangers were designed and manufactured using 3D printing technology. Their performance was evaluated from both thermal and hydrodynamic perspectives, the flow characteristics were investigated in detail. The results show that a fractal-tree-like heat exchanger can improve hydrodynamic performance, reduce pressure drops and has great heat transfer ability. In general, the fractal-tree-like heat exchanger has a comprehensive advantage over the traditional spiral-tube exchangers as it has a higher value of coefficient of performance (COP). Furthermore, the 3D printing provides a visual, efficient, and precise approach in the present research