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Augmented reality (AR) allows the real and digital worlds to converge and overlap in a new way of observation and understanding. The architectural field can significantly benefit from AR applications, due to their systemic complexity in terms of knowledge and process management. Global interest and many research challenges are focused on this field, thanks to the conjunction of technological and algorithmic developments from one side, and the massive digitization of built data. A significant quantity of research in the AEC and educational fields describes this state of the art. Moreover, it is a very fragmented domain, in which specific advances or case studies are often described without considering the complexity of the whole development process. The article illustrates the entire AR pipeline development in architecture, from the conceptual phase to its application, highlighting each step’s specific aspects. This storytelling aims to provide a general overview to a non-expert, deepening the topic and stimulating a democratization process. The aware and extended use of AR in multiple areas of application can lead a new way forward for environmental understanding, bridging the gap between real and virtual space in an innovative perception of architecture.

This study explores the impact of generative artificial intelligence (AI) teaching assistants on teachers' knowledge workflows through a multimodal interaction log analysis framework, combining petri net modeling and causal inference methods. The research reveals that generative AI significantly enhances task efficiency by reducing node compression rates (up to 28.9%) and optimizing process links (up to 35.1%), particularly in structured tasks like resource pushing. It also alleviates teachers' cognitive load, which is evidenced by a drop in subjective scores from 5.9 to 4.8 during interactive feedback stages. Key findings highlight a positive correlation between interaction frequency and structural optimization, with higher AI usage intensifying task compression effects. The study provides actionable insights for integrating generative AI into educational systems while addressing limitations in complex task modeling and behavioral data granularity.

The industrial internet depends on the development of cloud computing, artificial intelligence, and big data analysis. Intelligent fusion is dependent on the architecture and security features of the industrial internet. Firstly, the paper studies the infrastructure mode that needs to be solved urgently in the industrial internet and provides a possible infrastructure mode and related security evaluation system. Secondly, it analyses the digital transformation process with the case of G.E.os industrial nternet development practice. It clarifies that G.E. is forming a new value closed-loop through digital and strategy mixed channels. Thirdly, industrial internet security research is described within multiple viewpoints based on industrial internet applications, the security service and security assurance defense systemos architecture, and the non-user entrance probability model. Finally, the paper illustrates the changes in knowledge workflow and social collaboration caused by the industrial internet under intelligent manufacture.

As the result of business process automation, more and more knowledge is codified and stored in knowledge repositories and scattered in employees’ computers across functionally and geographically separated business units. While several alternative mechanisms such as chat rooms, search engines, recommender systems exist to retrieve and access knowledge, satisfying a knowledge requirement often involves the coordination of multiple tasks and the use of several technologies. However, there is no existing technology for orchestrating various collaboration, communication and information retrieval components to satisfy the knowledge needs in corporations. In order to address this gap, we propose the approach of pattern-based knowledge workflow that can enable the automation of knowledge flows across an organization. In this paper, we present an overview of the pattern-based knowledge workflow approach and propose extensions to the conventional workflow paradigm. We demonstrate the feasibility of implementing knowledge workflows by means of a Business Process Execution Language (BPEL) specification for executing knowledge workflows in a business setting. We then discuss the engineering challenges and research issues that need to be addressed to further develop this approach.

Organizations require ways to efficiently distribute information such as news releases, seminar announcements, and memos. While the machinery for information storage, manipulation, and retrieval exists, research dealing directly with its distribution in an organizational context is scarce. In this paper, we address this need by first examining the pros and cons of the conventional \"mailing lists\" approach and then proposing new workflow mechanisms that improve the efficiency and effectiveness of information distribution through e-mail. The proposed approach is relevant to other information distribution approaches beyond e-mail. The main contributions of this study include: (1) offering a workflow perspective on organizational information distribution; (2) analysis of workflows in two new information distribution methods based on dynamic mailing lists and profile matching, respectively; and (3) proposing a new way of matching supply and demand of information that extends existing information filtering algorithms.

This paper puts forth a vision and an architecture for a community knowledge evolution system. We propose augmenting a multimedia document repository (digital library) with innovative knowledge evolution support, including computer-mediated communications, community process support, decision support, advanced hypermedia features, and conceptual knowledge structures. These tools, and the techniques developed around them, would enable members of a virtual community to learn from, contribute to, and collectively build upon the community's knowledge and improve many member tasks. The resulting Collaborative Knowledge Evolution Support System (CKESS) would provide an enhanced digital library infrastructure serving as an ever-evolving repository of the community's knowledge, which members would actively use in everyday tasks and regularly update.

This paper aims at presenting the integrated platform for transferring knowledge and skills in agro-food sector, as a tool for facilitating information exchange between stakeholders: businesses, centres of research, educational and research in the food and agro-business sector, centres of business incubation in the food sector, clusters of SMEs, technology transfer centres. The methodology consists of a web application, whose features are detailed. Main results of research show that the platform generates savings of time and financial resources through a simplified procedure and provides opportunities for documentation and collaboration correlated to the real needs of users.

Knowledge extraction through machine learning techniques has been successfully applied in a large number of application domains. However, apart from the required technical knowledge and background in the application domain, it usually involves a number of time-consuming and repetitive steps. Automated machine learning (AutoML) emerged in 2014 as an attempt to mitigate these issues, making machine learning methods more practicable to both data scientists and domain experts. AutoML is a broad area encompassing a wide range of approaches aimed at addressing a diversity of tasks over the different phases of the knowledge discovery process being automated with specific techniques. To provide a big picture of the whole area, we have conducted a systematic literature review based on a proposed taxonomy that permits categorising 447 primary studies selected from a search of 31,048 papers. This review performs an extensive and rigorous analysis of the AutoML field, scrutinising how the primary studies have addressed the dimensions of the taxonomy, and identifying any gaps that remain unexplored as well as potential future trends. The analysis of these studies has yielded some intriguing findings. For instance, we have observed a significant growth in the number of publications since 2018. Additionally, it is noteworthy that the algorithm selection problem has gradually been superseded by the challenge of workflow composition, which automates more than one phase of the knowledge discovery process simultaneously. Of all the tasks in AutoML, the growth of neural architecture search is particularly noticeable.

A comprehensive knowledge of the types and ratios of microbes that inhabit the healthy human gut is necessary before any kind of pre-clinical or clinical study can be performed that attempts to alter the microbiome to treat a condition or improve therapy outcome. To address this need we present an innovative scalable comprehensive analysis workflow, a healthy human reference microbiome list and abundance profile (GutFeelingKB), and a novel Fecal Biome Population Report (FecalBiome) with clinical applicability. GutFeelingKB provides a list of 157 organisms (8 phyla, 18 classes, 23 orders, 38 families, 59 genera and 109 species) that forms the baseline biome and therefore can be used as healthy controls for studies related to dysbiosis. This list can be expanded to 863 organisms if closely related proteomes are considered. The incorporation of microbiome science into routine clinical practice necessitates a standard report for comparison of an individual's microbiome to the growing knowledgebase of \"normal\" microbiome data. The FecalBiome and the underlying technology of GutFeelingKB address this need. The knowledgebase can be useful to regulatory agencies for the assessment of fecal transplant and other microbiome products, as it contains a list of organisms from healthy individuals. In addition to the list of organisms and their abundances, this study also generated a collection of assembled contiguous sequences (contigs) of metagenomics dark matter. In this study, metagenomic dark matter represents sequences that cannot be mapped to any known sequence but can be assembled into contigs of 10,000 nucleotides or higher. These sequences can be used to create primers to study potential novel organisms. All data is freely available from https://hive.biochemistry.gwu.edu/gfkb and NCBI's Short Read Archive.

Edge computing is a novel technology, which is closely related to the concept of Internet of Things. This technology brings computing resources closer to the location where they are consumed by end-users—to the edge of the cloud. In this way, response time is shortened and lower network bandwidth is utilized. Workflow scheduling must be addressed to accomplish these goals. In this paper, we propose an enhanced firefly algorithm adapted for tackling workflow scheduling challenges in a cloud-edge environment. Our proposed approach overcomes observed deficiencies of original firefly metaheuristics by incorporating genetic operators and quasi-reflection-based learning procedure. First, we have validated the proposed improved algorithm on 10 modern standard benchmark instances and compared its performance with original and other improved state-of-the-art metaheuristics. Secondly, we have performed simulations for a workflow scheduling problem with two objectives—cost and makespan. We performed comparative analysis with other state-of-the-art approaches that were tested under the same experimental conditions. Algorithm proposed in this paper exhibits significant enhancements over the original firefly algorithm and other outstanding metaheuristics in terms of convergence speed and results’ quality. Based on the output of conducted simulations, the proposed improved firefly algorithm obtains prominent results and managed to establish improvement in solving workflow scheduling in cloud-edge by reducing makespan and cost compared to other approaches.