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With the digital transformation of the construction industry, the need to improve construction business process collaboration and automation is increasing. However, as construction projects usually involve many stakeholders with complex relationships and insufficient mutual trust, the existing technologies cannot fulfill the requirements of the construction industry. This paper explores the integration of blockchain, smart contracts, and process automation technologies into construction business processes, shedding light on both managerial and technical challenges. The study introduces a comprehensive technical framework meticulously designed to align with the intricate nature of construction practices while focusing on the resolution of legal complexities associated with smart contract applications and the facilitation of process collaboration and automation. The framework comprises three core modules: (1) action definition and extension to process models, (2) standardized mapping for automatic smart contract generation, and (3) smart contract visualization for reliable process collaboration and automation. This multifaceted approach caters to the specific needs of construction management, standardization, interoperability, and visualization. The framework's practicality is further evaluated through real-world testing within a construction payment case, effectively showcasing its efficacy and applicability in tangible business scenarios. While this paper represents a significant step forward in addressing construction business process collaboration and automation challenges, it acknowledges the necessity for ongoing research and development to refine and expand these innovative solutions to meet the evolving demands of construction management. Practical Applications: Currently, there is a significant escalation in the severity of disputes within the construction industry. The adoption of blockchain technology allows for the redistribution of trust within a construction project, shifting reliance from individuals to the system itself for better collaboration and fewer disputes. Although blockchain can help foster a more secure and transparent environment, at the process level, an efficient workflow engine is needed to activate processes in this environment. Therefore, this study proposed an approach to generating smart contract codes based on construction business processes in a standardized way and further visually executing these codes for the processes with historical transactions stored in blockchain. This approach can reduce the effort of smart contract programming and improve the standardization and credibility of smart contract generation. It can also streamline the procedure from construction business process design to final execution via predefined and visual modules. Meanwhile, the generation prototype is open sourced to provide transparency and further enhance the approach. To indicate practical implications in the real world, this study provided a case of construction payment, but the solution is not limited to the payment scenario. [ABSTRACT FROM AUTHOR]

As an extension of networked control systems, cloud control systems (CCSs) have emerged as a new control paradigm to improve the service quality of emerging control missions, such as data-driven modeling and automated vehicles. Existing studies have used the workflow-based restructured method to optimize the computation-intensive algorithms in the CCSs. However, the challenges here are how to define and submit these algorithms' workflows as cloud services and execute these algorithms' workflows in a containerized manner. Based on these challenges, we propose a containerized solution for the control-algorithm-as-a-service (C3aS) in the CCSs, namely ControlService. It offers the control algorithm as a cloud workflow service and uses a customized workflow engine to realize the containerized execution. First, we employ a cloud workflow representation method to define a control algorithm into an abstract cloud workflow form. Afterward, we provide a cloud service representation of the abstract cloud workflow. Next, we design a workflow engine and submit the cloud service to this workflow engine to implement containerized execution of this cloud service in the CCSs. In the experiment, we discuss the cloud service form and containerized implementation of the subspace identification method. Experimental results show that the proposed ControlService has significant performance advantages in computational time, reduction percentage, and speedup ratio compared with the baseline method. [ABSTRACT FROM AUTHOR]

As a vital component in the shipping industry, coastal container terminals must be developed in a sustainable manner. The container receiving process is a crucial aspect of container loading and transportation and is the cornerstone of container terminal operations. This process is characterized by its discrete random events. To model this process, the testbed was designed and developed using a Hierarchical Generalized Stochastic Petri Net (HGSPN) workflow engine to drive the data and simulate the container receiving process through the interplay of resources and changes. Random indexing was utilized to mimic the discrete randomness of the operations. The performance of the container receiving process was validated and evaluated through the construction of an operational energy and efficiency‐focused container receiving evaluation model. Results showed that the intelligent container receiving system, tested on the in‐loop platform, can effectively evaluate the strengths and weaknesses of container receiving, leading to continuous improvement of the system and demonstrating its significant practical value. [ABSTRACT FROM AUTHOR]

Internet information technology has brought challenges to the integration of college and university Civics in student education and management work, and at the same time, it also provides an opportunity for the integration of information technology. In this paper, on the basis of two kinds of nodes of basic Petri nets, the library and change, we constructed the workflow of identification Petri nets oriented to the education and management of students in the Civic Government of colleges and universities. The workflow engine is designed as the center of gravity of the reference model, consisting of six basic elements of student management. In the information fusion workflow, a multi-source information fusion method based on CNN and LSTM is proposed to further integrate Civics into student education management. The response time of the student management action flow is shorter than the retrieval time based on the original mechanism, and the average response time is reduced by 57.4 ms when the workflow reaches 5000. The fusion of Civic Politics and education management in the context of three-whole-parenting optimizes the management work method.

In recent years, cloud-native technology has become popular among Internet companies. Microservice architecture solves the complexity problem for multiple service methods by decomposing a single application so that each service can be independently developed, independently deployed, and independently expanded. At the same time, domestic industrial Internet construction is still in its infancy, and small and medium-sized enterprises still face many problems in the process of digital transformation, such as difficult resource integration, complex control equipment workflow, slow development and deployment process, and shortage of operation and maintenance personnel. The existing traditional workflow architecture is mainly aimed at the cloud scenario, which consumes a lot of resources and cannot be used in resource-limited scenarios at the edge. Moreover, traditional workflow is not efficient enough to transfer data and often needs to rely on various storage mechanisms. In this article, a lightweight and efficient workflow architecture is proposed to optimize the defects of these traditional workflows by combining cloud-edge scene. By orchestrating a lightweight workflow engine with a Kubernetes Operator, the architecture can significantly reduce workflow execution time and unify data flow between cloud microservices and edge devices. [ABSTRACT FROM AUTHOR]

A robust medical image computing infrastructure must host massive multimodal archives, perform extensive analysis pipelines, and execute scalable job management. An emerging data format standard, the Brain Imaging Data Structure (BIDS), introduces complexities for interfacing with XNAT archives. Moreover, workflow integration is combinatorically problematic when matching large amount of processing to large datasets. Historically, workflow engines have been focused on refining workflows themselves instead of actual job generation. However, such an approach is incompatible with data centric architecture that hosts heterogeneous medical image computing. Distributed automation for XNAT toolkit (DAX) provides large-scale image storage and analysis pipelines with an optimized job management tool. Herein, we describe developments for DAX that allows for integration of XNAT and BIDS standards. We also improve DAX's efficiencies of diverse containerized workflows in a high-performance computing (HPC) environment. Briefly, we integrate YAML configuration processor scripts to abstract workflow data inputs, data outputs, commands, and job attributes. Finally, we propose an online database–driven mechanism for DAX to efficiently identify the most recent updated sessions, thereby improving job building efficiency on large projects. We refer the proposed overall DAX development in this work as DAX-1 (DAX version 1). To validate the effectiveness of the new features, we verified (1) the efficiency of converting XNAT data to BIDS format and the correctness of the conversion using a collection of BIDS standard containerized neuroimaging workflows, (2) how YAML-based processor simplified configuration setup via a sequence of application pipelines, and (3) the productivity of DAX-1 on generating actual HPC processing jobs compared with earlier DAX baseline method. The empirical results show that (1) DAX-1 converting XNAT data to BIDS has similar speed as accessing XNAT data only; (2) YAML can integrate to the DAX-1 with shallow learning curve for users, and (3) DAX-1 reduced the job/assessor generation latency by finding recent modified sessions. Herein, we present approaches for efficiently integrating XNAT and modern image formats with a scalable workflow engine for the large-scale dataset access and processing. [ABSTRACT FROM AUTHOR]

In recent years, cloud-native technology has become popular among Internet companies. Microservice architecture solves the complexity problem for multiple service methods by decomposing a single application so that each service can be independently developed, independently deployed, and independently expanded. At the same time, domestic industrial Internet construction is still in its infancy, and small and medium-sized enterprises still face many problems in the process of digital transformation, such as difficult resource integration, complex control equipment workflow, slow development and deployment process, and shortage of operation and maintenance personnel. The existing traditional workflow architecture is mainly aimed at the cloud scenario, which consumes a lot of resources and cannot be used in resource-limited scenarios at the edge. Moreover, traditional workflow is not efficient enough to transfer data and often needs to rely on various storage mechanisms. In this article, a lightweight and efficient workflow architecture is proposed to optimize the defects of these traditional workflows by combining cloud-edge scene. By orchestrating a lightweight workflow engine with a Kubernetes Operator, the architecture can significantly reduce workflow execution time and unify data flow between cloud microservices and edge devices.

Scientific problems can be formulated as workflows to allow them to take advantage of cluster computing resources. Generally, the assumption is that the greater the resources dedicated to completing these tasks the better. This assumption does not take into account the energy cost of performing the computation and the specific characteristics of each workflow. In this paper, we present a unique approach to evaluating the energy consumption of scientific workflows on compute clusters. Two workflows from different domains, Astronomy and Bioinformatics, are presented and their execution is analyzed on a cluster of low powered small board computers. The paper presents a theoretical analysis of an energy-aware execution of workflows that can reduce the energy consumption of workflows by up to 68% compared to normal execution. We demonstrate that there are limitations to the benefits of increasing cluster sizes and there are trade-offs when considering energy vs. performance of the workflows and that the performance and energy consumption of any scientific workflow is heavily dependent on its underlying structure. The study concludes that the energy consumption of workflows can be optimized to improve both aspects of the workflow and motivates the development of an energy-aware scheduler. [ABSTRACT FROM AUTHOR]

Digital Twins (DT) in industrial cyber-physical systems are the key enabling technology for Industry 4.0. Services are an essential part of almost every DT concept, but their interaction is usually implementation-specific since no common guidelines are available. This work identifies some fundamental requirements for a DT service framework based on applications identified in corresponding literature. Based on these requirements, a service framework architecture is proposed. The architecture utilizes Semantic Web technology and a workflow engine for service orchestration to support the fulfilment of the identified requirements. As a case study for sensor data evaluation of an industrial process, a proof-of-concept implementation is presented, showing the feasibility and suitability of the proposed DT service framework architecture. [ABSTRACT FROM AUTHOR]

Copyright of Journal of National University of Defense Technology / Guofang Keji Daxue Xuebao is the property of NUDT Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Recent years have witnessed an emerging trend for improving disease treatment by forming multidisciplinary medical teams. The collaboration among specialists from multiple medical domains has been shown to be significantly helpful for designing comprehensive and reliable regimens, especially for incurable diseases. Although this kind of multi-disciplinary treatment has been increasingly adopted by healthcare providers, a new challenge has been introduced to the decision-making process--how to efficiently and effectively develop final regimens by searching for candidate treatments and considering inputs from every expert. In this article, we present a sophisticated decision support system called MdtDSS (a decision support system (DSS) for multi-disciplinary treatment (Mdt)), which is particularly developed to guide the collaborative decision-making in multi-disciplinary treatment scenarios. The system integrates a recommender system that aims to search for personalized candidates from a large-scale high-quality regimen pool and a voting system that helps collect feedback from multiple specialists without potential bias. Our decision support system optimally combines machine intelligence and human experience and helps medical practitioners make informed and accountable regimen decisions. We deployed the proposed system in a large hospital in Shanghai, China, and collected real-world data on large-scale patient cases. The evaluation shows that the proposed system achieves outstanding results in terms of high-quality multi-disciplinary treatment. [ABSTRACT FROM AUTHOR]

Copyright of Dokuz Eylul University Muhendislik Faculty of Engineering Journal of Science & Engineering / Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi is the property of Dokuz Eylul Universitesi Muhendislik Fakultesi Fen ve Muhendislik Dergisi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Digital Twins (DT) in industrial cyber-physical systems are the key enabling technology for Industry 4.0. Services are an essential part of almost every DT concept, but their interaction is usually implementation-specific since no common guidelines are available. This work identifies some fundamental requirements for a DT service framework based on applications identified in corresponding literature. Based on these requirements, a service framework architecture is proposed. The architecture utilizes Semantic Web technology and a workflow engine for service orchestration to support the fulfilment of the identified requirements. As a case study for sensor data evaluation of an industrial process, a proof-of-concept implementation is presented, showing the feasibility and suitability of the proposed DT service framework architecture.

In general, lots of software need to be cooperated together for particular object in scientific experiments and engineering domains. This paper described a new scientific workflow application platform in HPC environment. This platform contained an engine with high concurrency and asynchronous framework to process workflow application. Scheduler, planner and engine were decoupled from each other, which allowed that the three components could develop independently: scheduler for scheduling algorithms implementation, planner for collecting the scheduling information and engine for workflow driver. Scheduler and planner used resource advance reservation and local pooling mechanism to increase the performance of workflow execution. This paper also implemented and compared five scheduling algorithms as to their performance on testing graph set, and got some useful advices of algorithm selection. Real application shows that engine can support various execution strategies and resource scheduling solution can help increase efficiency of workflow execution in supercomputing environment. [ABSTRACT FROM AUTHOR]

Through the analysis of workflow management system based on Web, the work, following WFMC workflow model, proposed a universal workflow management system with the combination of specific application requirements of enterprises. Workflow management system based on Web was described from system function, software structure, system structure, workflow engine and security. The actual case of the design of aeronautical structure showed that workflow system performance can be greatly improved by using Web technology. [ABSTRACT FROM AUTHOR]