Understanding the residents’ routine and repetitive behavior patterns is important for city planners and strategic partners to enact appropriate city management policies. However, the existing approaches reported in smart city management areas often rely on clustering or machine learning, which are ineffective in capturing such behavioral patterns. Aiming to address this research gap, this article proposes an analytical framework, adopting sequential and periodic pattern mining techniques, to effectively discover residents’ routine behavior patterns. The effectiveness of the proposed framework is demonstrated in a case study of American public behavior based on a large-scale venue check-in dataset. The dataset was collected in 2020 (during the global pandemic due to COVID-19) and contains 257 561 check-in data of 3995 residents. The findings uncovered interesting behavioral patterns and venue visit information of residents in the United States during the pandemic, which could help the public and crisis management in cities.

The existing multi-person collaborative design scheme of Building Information Modeling (BIM) integrated with blockchain faces problems such as poor reliability of BIM drawing, inconsistent drawing information, redundant information, and inaccurate protection of copyright interests. This paper proposes a multi-person collaborative design model for BIM drawing that combines blockchain and InterPlanetary File System (IPFS). This model uses blockchain to store drawing design information to protect the copyright interests of designers and combines IPFS to ensure the reliability of drawing. A cycle division mechanism is designed to solve the problem of drawing information synchronization when multiple people collaborate in design. The Semantic Differential Transaction (SDT) method is used to achieve incremental update of drawing and reduce the information redundancy of the blockchain. Finally, a comparative analysis and validation evaluation of the scheme is carried out, and the usability of the scheme is illustrated with an illustrative example. The results show that: (1) proposed scheme is feasible for multi-person collaborative design; (2) proposed scheme can effectively ensure the reliability of drawing and reduce the redundancy of blockchain information, so as to achieve copyright protection for designers.

With the rapid development of artificial intelligence, the Internet of Things (IoT) can deploy various machine learning algorithms for network and application management. In the IoT environment, many sensors and devices gener... | Find, read and cite all the research you need on Tech Science Press

Enterprise risk management holds significant importance in fostering sustainable growth of businesses and in serving as a critical element for regulatory bodies to uphold market order. Amidst the challenges posed by intricate and unpredictable risk factors, knowledge graph technology is effectively driving risk management, leveraging its ability to associate and infer knowledge from diverse sources. This review aims to comprehensively summarize the construction techniques of enterprise risk knowledge graphs and their prominent applications across various business scenarios. Firstly, employing bibliometric methods, the aim is to uncover the developmental trends and current research hotspots within the domain of enterprise risk knowledge graphs. In the succeeding section, systematically delineate the technical methods for knowledge extraction and fusion in the standardized construction process of enterprise risk knowledge graphs. Objectively comparing and summarizing the strengths and weaknesses of each method, we provide recommendations for addressing the existing challenges in the construction process. Subsequently, categorizing the applied research of enterprise risk knowledge graphs based on research hotspots and risk category standards, and furnishing a detailed exposition on the applicability of technical routes and methods. Finally, the future research directions that still need to be explored in enterprise risk knowledge graphs were discussed, and relevant improvement suggestions were proposed. Practitioners and researchers can gain insights into the construction of technical theories and practical guidance of enterprise risk knowledge graphs based on this foundation.

With the development of cloud manufacturing, the automation of storage scheduling becomes popular in the steel industry. However, the high customization of steel plates makes the storage scheduling too complex to be optimized. To overcome this challenge, we propose to utilize the big data of steel plate orders and warehouse status to optimize the storage scheduling of steel plates. The agglomerative hierarchical clustering is first adopted to reduce the complexity of excessive steel plate specifications, then an optimization problem is defined to formulate the storage scheduling of steel plates with safety. A two-stage heuristic (TSH) algorithm is proposed to solve the optimization problem with low complexity. In TSH, steel plates are first assigned to multiple stacks, and then the arrangement of each stack is determined. Experiments are executed based on a cloud manufacturing platform for steel plates production and storage, and the results prove the effectiveness of the proposed works.